Schrodinger Inc Aktie

Well, welcome. Good afternoon, everyone, to the first day of the conference here. I'm Craig Hettenbach. I cover health care technology and providers with Morgan Stanley. Very pleased to have with us the full team of Schrödinger here today. So we have CEO, Ramy Farid; CFO, Richie Jain; CTIO of Software, Pat Lorton; and then Karen Akinsanya, President of R&D. So thank you all for being here today.

Thanks for having us, Craig.

And I thought I'd just kick it off just as a jumping point, if I think about Schrodinger. You have a very rich history of a company that's been around for 35 years. And so I'd love to start with just kind of the most important development that kind of lead you up to today in terms of where the business is at.

Sure. On the scientific side, right? On the platform side. Yes. Well, as you said, we've been at this for 36 years actually since 1990, advancing a computational platform for design of molecules -- for designing molecules and predicting the properties of molecules. And one of the biggest advances is developing methods that allow us to actually accurately simulate what molecules are doing in the incredibly complex environment they're in. Molecules, drug molecules, for example, exist in water. Water turns out to be pretty sophisticated and the interactions of water with small molecules is complex. And then, of course, when small molecules bind to proteins, that's a very complex and highly dynamic process.

So we've developed all sorts of technologies for understanding the structures of protein, understanding the structure of proteins in water, understanding how molecules bind to proteins and what happens. And then the result of that is the ability to predict some incredibly complex properties such as how tightly a molecule binds to a protein. how soluble a molecule is, how permeable it is. These are incredibly important properties for developing drugs. A drug molecule has to be potent and selective and soluble and permeable and so on. And there's a huge amount of technology that sort of underlies the ability to use first principles physics to predict accurately the properties of molecules and essentially emulate experiment.

So it would probably not be a good use of time to get into the underlying physics that result in it, but I hope that gives you a general sense. Now since then, now once you have the sort of core engine, this physics engine, how do you amplify it? How do you get that kind of technology out to customers? So one is you have to be able to manage huge amounts of data. I mean running these physics calculations on massive scales, approaching trillions of molecules, you need a very robust enterprise system for storing all of this data, being able to analyze it. So we've done some really nice work on the engineering side to manage all of that data. And then the other thing is these physics-based calculations are computationally expensive.

And so -- but I said earlier that you have to be able to explore maybe trillions of molecules, certainly on the order of hundreds of billions of trillions of molecules. You can't do that using physics alone. So we put a significant effort into developing machine learning and AI methods that are trained on physics that amplify the physics to a scale that allows us to explore huge amounts of chemical space. So that's another very important aspect of the platform. So I think that's -- we're touching on sort of the main sort of breakthroughs and there's a lot more to do go ahead.

I'd say. And then, of course, you've talked about where we are today. And the next step, what we pride ourselves on is thinking what a computer is going to be able to do in 5 years and how do we start working on it now.

So when we started working on our first essentially agent 3 or 4 years ago, it didn't work, and we kept building it and improving it. And finally, as LLMs and agents reach the space they did, we built Bunsen, which is our new agent that can run all these workflows at a scale that was previously unpatentable. And -- but we got ahead of that. It took years of getting ready so that when the technology got there, we could do it, which we've done with GPUs in the past with physics. The cloud at scale we did with running physics at a larger scale. So we really look forward to the next generation of technology with science.

Great. And how do you think about that? It's that baseline of all the physics, the expertise, now you have this inflection in computation, just having those 2 pieces and what that means in terms of how you're positioned for the future?

Yes, yes. So okay. So a number of things. Of course, we're licensing that platform to pharma companies and they're experiencing now maybe for the first time, a significant impact on deploying computation to replace experiments, right, on a large scale. And that's resulting in growth in our software business. We also -- I hope I'm answering your question, but you'll stop me if I'm not. The other thing is we're -- and Karen can speak more to this, we're using that platform at scale at a really massive scale actually to advance a number of our own programs and either on our own behalf or in collaboration with pharma companies or biotech companies. In some cases, those are biotech companies that we have cofounded. Now the other thing that's important to mention, and I know this is a health care conference, so I'm just going to mention it very briefly.

The physics that underlies our platform is agnostic to the system. It works on small molecules, peptides, biologics, any class of proteins, but it also applies to design of materials. So that's another pretty important part of our business where we're using the same physics to design things like the molecules that go into batteries to make batteries work better or polymers that coat airplane wings or in design of electronics. I mean there's so many applications of that same physics. So I wanted to throw that in as well, even though the focus, of course, here is on drug discovery. Did I answer you?

You did. I think just to build on that, when you say kind of licensing it to pharma, how synergistic is it having a therapeutics group as well in terms of having both of these businesses?

I'll say something very quickly and hand it very quickly over to Karen. So in order to be able to develop technology that actually works and that impacts real projects. This is a space where you have to be using the technology yourself. And you learn so much from that, right? Not everything works, right? So software developers develop some technology and some of it works, some of it doesn't. And if you don't have real-world experience with it, where you're actually trying to develop a material or a drug, you can really fool yourself. The other thing is this is, by its nature, highly disruptive. This is -- there is a long-standing traditional way of doing drug discovery in pharma companies. And pharma companies are big companies that do things in a certain way and they're not going to change very easily.

And in order to convince them to change to do something completely different, it's very important that they see proof points. And those proof points have come from the incredible work of Karen's team in delivering development candidate after development candidate, after development candidate, successful exits of the new codes that we cofounded. And I think that validation has transformed the industry. It has convinced pharma companies, many of which, by the way, were investors in these new codes, convince pharma companies that, yes, this is the real thing, and we need to be investing in this and scaling it up. We may not know how to do it right now. Hopefully, Bunsen, our agent will help them with that. So it's both ways. It's knowing what technology to develop and then validating it. But... Sorry, I... I'm a little too [indiscernible].

I think you covered it. So we don't develop a platform in a vacuum. We actually develop it really working on real-world problems. And as Pat said, actually, we're always thinking a few years ahead to what is it that pharma is trying to solve for. And we're solving for that on our own behalf. some of our programs, but the majority of what we do is in collaboration with pharma companies, with biotechs. And it's really about solving difficult drug design challenges. One great example of that was our collaboration with -- on Pharma was going after a brain-penetrant version of a drug. We were working on E-sol, which is one of the modules in our platform. It was first used in that collaboration, and now it's broadly applicable and used by the whole industry.

And so the drug discovery team is involved in, yes, working on development candidates, but really cracking these problems and working very closely with our platform R&D team on the things that we think need to be solved. Predictive tox is a great example. That came out of one of the programs that we were working on where we wanted a structure of one of the off targets. And then we found we could scale that up, and it's now, again, a core part of our platform. And then separately, the therapeutics team, I guess, these assets are also generating some revenue for the company as we work with collaborators. And then there's the long-term view of owning a slice of quite a number of assets in the industry at this time.

Just to finally bring home the synergy of the business, Craig, it's no surprise that our largest collaboration partners are also our largest software customers. And it's that working together with us is what really unlocks both sides of the business.

Great. Well I think there's a pretty good buildup to one of the bigger debates across software and health care IT is just AI disruption, right? And whether it's competition from LLMs, whether it's things like vibe coding. So maybe we can kind of put a bow on it here in terms of some of the things you said to lead up, like how you're positioned at this kind of technology inflection? What are some of the things you're insulated from?

Yes. Yes. So I think there's 2 aspects to that. One is using AI vibe coding to actually code up a physics platform. That's one. And then the other is using AI in place of physics, like not trying to use AI to write physics solutions, but somehow bypass it. So let's take both of those. I think those are both things that are on people's minds. With regard to the latter, it's become very clear that the diversity and the scale of chemical space, which is essentially infinite. That is the number of ways in which you can combine organic elements into a drug like molecule materials is estimated to be more than the number of atoms in the universe. So let's just call that infinite.

So when you have a situation like that, the training set that's required to capture all of that diversity doesn't exist. It doesn't matter if you collect every single bit of experimental data that humans have ever generated from every single company, if you can even do that, which, of course, you can't. But even if you could, that's still literally a drop of water in the ocean, where the drop of water is all the information we know from experiment and the ocean is chemical space. So you can't learn much about the ocean by analyzing a drop of water. You would come to the conclusion there are no fish in the ocean, for example. I mean, just to pick a silly example.

So it's necessary to generate way larger data sets, and that's done through simulation, physics. And the scale -- the ability to generate data using physics is enormous compared to experiment. I mean we generate about 10 years' worth of experimental data in 1 day. 10 years, the amount of time it takes to generate a certain amount of data using experiment, you can do that in 1 day using computation. So that, I think, addresses this idea that AI alone can predict the properties of arbitrary molecules. Now it's pretty good at predicting properties of molecules that look like what you've already predicted, but that's not very interesting. That's not drug discovery. Drug discovery is about finding novel molecules. Now with regard to -- maybe, Pat, it would be great for you to address, I think, the other question, right, about vide coding our entire platform. And do you want to comment on that?

Yes. I mean one thing that's really important when you talk about vide coding is that -- and we use -- by the way, we use these coding tools heavily. We love them. They're critically important in writing code at the speed we can today. But when you're talking about bigger solutions, one thing that's really an anchor there is you have to have a testable solution that can run pretty quickly because the agent has to be able to iterate on the testable solution. just running one validation run of our software suite just to confirm that the results have stayed consistent, takes a supercomputer days. So that's not something where you're going to turn the vide code off and say, Hey, write an engine that's good as Schrödinger because testing whether it's as good as the scientific validation test, if they even have them, by the way, which they don't, it took us years to build those tests is enormous.

So it's not something that's actually very amenable to vide coding. You can do bits of it vide coding, which we're doing. We're accelerating our own development. That's -- we're able to build products even faster than we have in the past. I will also kind of connecting back to the earlier question, how is it shifting funding allocation inside pharma you are asking. And we've actually been very pleasantly surprised to see it go the opposite of the way many people have feared, whereas money is pouring into AI solutions and people are able to grab that budget and use it for our solution. So Bunsen, for instance, is a solution now that can compete for these AI budgets that are coming in. It's not necessary that those AI budgets are pulling away from their other spend and reducing spend in other places, but we're pulling money out of AI budgets.

That's the pattern we're seeing.

Yes. Great. So I want to dig into the customer base. And Rich, you mentioned some of your largest pharma customers are also on the software side, too. So give us a feel in terms of your traction with kind of top 20 large global pharma, also things that may be happening on the biotech side?

Sure. Yes. We've been -- the top 20 pharma customers have been our customers for decades at this point. And we're embedded within all of those organizations. There does remain an adoption gap between our largest customers and smaller customers. And of course, we're looking to close that with the addition of new products. Biotech is a substantial contributor to our business. It's been one of the challenges over the past few years, not surprisingly, given where that market has been, but it's starting to open up, as you're all aware. And as that flows through into capital recycle and forming new companies, we expect that to be a beneficiary to Schrodinger. But in general, across all of life sciences, particularly big cap pharma but across biotech, we're well embedded within the customer base and focused on growing adoption. Bunsen for us is a big beneficiary and enabler of that change.

Across material science, that's about 10% of our business today across a variety of end markets. We continue to grow that business and have it become a more meaningful contributor to the overall business.

Got it. Any example of things you're doing to kind of deepen the relationships with customers? And how do you think about just how retention is for your business?

Yes. Retention has been a really strong point for us for a number of years, measured across gross dollar retention or net dollar retention. In addition to just increasing adoption, what we've done, in particular, this year is rolled out a bunch of new products. In addition to Bunsen, we've rolled out predictive Tox, we've rolled out Crystal structure prediction. These are additional tools within the discovery workflow that are expanding our domain of expertise and also reaching additional budgets that we haven't had access to in the past.

Got it. And Pat, you mentioned some budget freeing up on the AI, which is great incremental. I guess my question more broadly, as you look at kind of sales cycles in the marketplace, anything different that you're seeing in the market? Like how is your sense of just the demand backdrop today?

Yes. I mean I'd say I think everyone in this room is probably feeling XBI riding at highs. There's a lot more money going to biotech right now. The headwinds of the last few years are definitely easing, and that helps on that side of things. We're also seeing big pharma really getting pressured to adopt new technology. And we are ultimately the most trusted partner in this space. So to tie your 2 questions together, you're asking about us expanding our relationship with big pharma, something like predictive tox is something we weren't in there before. We weren't with those groups. But because we've been a trusted partner, that's a place for us to expand. The same is true for biologics. We are the premier solution for small molecule informatics with our LiveDesign products.

And in the last year, we've introduced LiveDesign Biologics, which is catching on at several big pharma, and we expect it to penetrate just as much and get us more into the biologics space. So we both are trying to get them to embrace our technology at much greater scale, and we're trying to broaden our relationship across more spaces we weren't in historically.

Great. I do want to spend a couple of minutes just on the model transition kind of on-prem to the cloud. And maybe we can start with kind of why you're undertaking that, kind of why that's the right move and we can build on from there.

Yes. So the transition that we announced is moving from on-prem to hosted over a 3-year period. As a reminder, our starting point was about 25% hosted on our software revenue. Our goal is to get to 75% hosted over the 3-year time period. I think the reason we announced this change is we've been slowly moving in this direction for a number of years. We've been able to support our largest customers with hosted solutions. And it felt like the time for us to really make this transition in a more accelerated fashion, given the acceptance of hosted solutions by our customers. And also from a reporting perspective, it creates a cleaner financial profile for Schrodinger given that hosted revenues are recognized ratably over the term of the contract as opposed to on-prem revenues, which are mostly pulled upfront.

So a cleaner financial profile, customers are accepting it. It also helps us better understand how the customer is using the technology. We can support them better by seeing how they're utilizing it, address support issues and also be in a better position for renewals at renewal season.

Got it. And then how has customer feedback been to date? Any resistance? Or how are you working through customers as you go through this transition?

Yes, I'll start and then ask Pat to comment as well. I think we are pleased with the progress we've been making on the transition to hosted in Q2, hosted was 47% of revenue. I think that was ahead of where we expected to be at this point. We've been able to transition large customers ahead of renewal dates. So I think overall, I'm pleased at how it's representing itself into the financials. Pat, you should comment about how [indiscernible].

Yes. We've gotten shockingly -- I mean, it shouldn't be too shocking. We took it. We knew we hoped going into it, but essentially no resistance whatsoever. Thankfully, this is now a paradigm that people are very used to. Windows is phoning home, Adobe is phoning home. Everything is doing this for licensing. So it really is just about going through the process that companies have rather than convincing them to do something new and crazy.

Great. And I wanted to touch on just some of the momentum in Q2, ACV was up 27% year-over-year, kind of some of the key drivers of that for you guys.

Sure. Yes. I mean Q2 was a great quarter. I think first half of this year compared to last year, we were up 19%. So it's evidencing itself through a couple of quarters. We've seen broad growth across pharma, biotech and material sciences and also contribution from our new products, including Bunsen and Predictive Tox. So overall, I think we're very pleased with the profile on the software side of the business. Drug discovery had a great quarter in Q2. We increased guidance for the year by $10 million as a result of that as well. From an operating expense perspective, we're in line with what we've said publicly, which is we expect this year OpEx to be lower than last year. And through the first half of the year, we're down about 6%.

So overall, I think we're tracking towards our goals across growth in the business, transition to hosted as well as a disciplined operating expense profile.

Got it. And the point of OpEx down 6% and which sets you up also some leverage going forward, any kind of key drivers there that you would call out in terms of things you're doing with the business?

Yes. We've -- those efficiencies have come from headcount reductions as well as reduced professional services and CRO spending in the therapeutics team. And as evidence of that, we just announced a transaction last week to form a new company called Tectora, which I'll let Karen comment on in just a second. But that's been the source of the OpEx efficiency. And this is really the full year annualization of a lot of the actions we took last year in setting forward the strategy that we did a few months ago. But do you want to comment on Tectora?

Yes. I mean I think last year, we announced that we would not be taking programs into the clinic. What we are focused on is moving programs through discovery to a certain point and then either partnering them with pharma. In this case, we actually were able to move 2 of those programs and some of that CRO costs that Richie talked about is really our expense through to development candidate. That will now be transferred into the fundraise that came around Tectora. So last week, we announced 2 of our immunology programs, moving into a spinout called Tectora, which raised $55 million, which will take that program -- fund that program through discovery, and we hope into early clinical development as well.

That obviously takes the CRO cost off our balance sheet, and we actually have a pretty significant equity stake in that company. So we're excited about the programs and also about the launch of what is now, I think, our eighth newCo under the Schrodinger umbrella. And so looking forward to seeing how that develops.

Great. I definitely want to come back to that in terms of on the biotech side. But before we do and just building on some of the strength in the business first half of the year, Q4 is typically the seasonally strongest from a bookings activity. Anything you would call out like this year versus prior years or things you're watching in the market that you want to make sure you're executing on?

I mean I'll start. I think Ramy and Pat, you should add again. But yes, as you noted, Q4 is generally more than 50% of our business for the year. I think the backdrop sitting here today on September 14 compared to a year ago is just incredibly stronger across all of our customer types, big pharma, biotech and material sciences. So in general, we've been feeling that through the first 8 months of the year. and we expect that trend to continue. But you should add kind of at any specific levels, anything further?

Yes. I mean I think there's just kind of what we've been talking about. There's a lot of excitement around using computation at scale. There's clear recognition that the technology actually works. It's having an impact. There's just all these success stories of molecules, some of which the pharma companies have had to purchase at very high prices that were designed using our platform. By the way, one of our least favorite comment that you keep hearing AI designed molecules, and that's not a thing. Of course, we've been designing molecules using computation for a long time. They're already successful. It's not like that hasn't happened yet, right? We're not sitting around waiting for the first time that a so-called AI-generated molecule is successful.

That's still sort of thing to track. So yes, it's just this very positive kind of atmosphere where there's an appreciation for the technology working. And now there's a sort of how are we going to deploy it at scale. And so we kind of came out with Bunsen, this agent at just the right time where it seems to address sort of the last remaining barrier to using the technology at scale, which is sort of orchestrating these really complex workflows. To us, it doesn't seem that complicated. We've been developing it and using it for decades. But when it turns out when we release it to our customers, it's a lot more complicated than we may have appreciated.

So it's really great that we found this way of identifying it at just the right time when agents were being used in all sorts of other parts of our lives. I mean now you hear about agents running companies, right, just completely orchestrating right, the whole company. Well, now it's time to start thinking about doing that on the drug discovery side.

Yes. I mean we're just seeing tremendous interest in Bunsen. Yes. of any product we've been trading of 20 years, more than any product we've ever released. We have lines at our booths. It's pretty cool to see.

Got it. And I know one of your key partners, Bristol-Myers is kind of early on Bunsen. Just talk about that in terms of a large high-profile customer kind of moving on and what that means to other opportunities.

Yes. Yes, it's a great sign. Now BMS has been a long-standing partner. They've had access to our technology at a large scale, but they're experiencing the same kind of things that a lot of other pharma companies are experiencing. In fact, other companies are experiencing even more, which is a sort of reduction in the number of people that can actually run these technologies. And so they recognize the need to amplify the experts they already have instead of hiring another 10 people that don't even exist, right? Now you can hire -- you have these core experts that now can have a co-scientist. I mean I think that's a great word to describe these agents, the co-scientists, a whole bunch of modelers that don't sleep, that can work through the night and check on things and restart jobs if they don't fail.

And you don't -- think about how much time you lose when something dies in middle of the night and then you wasted another day, right, and this kind of stuff, just keeping up. It's a game changer. So -- and as part of that as we announced, and this is a pretty important part of this. So in order to take advantage of an agent, you need to have a lot more of the technology. Otherwise, it's kind of what's the point, right? So as part of the partnership, they got access to a significant amount of the technology, more throughput so they can run -- they can predict the properties of more molecules computationally, which makes it easier to solve the multiparameter optimization problem.

You're more likely to find that special drug molecule that has all the properties if you're testing more and more molecules. So we're very excited about it. They're very excited. This got -- it involves the AI budgets involved the computational people, BD. I mean, this was a big strategic partnership because I think they see that this is the future. And as Pat was basically saying, I think a lot of other companies are recognizing the same thing.

Great. And then Karen, you alluded to predictive toxicology before. There's been developments at the FDA on animal testing. How are you thinking about this over a multiyear horizon in terms of the opportunity that might present itself?

Yes. I think as you point out, the FDA has announced a couple of years ago now the alternative models for predictive tox. Obviously, there's a spectrum of those. You've got organoids. On the in silico end, we've now developed a structure-based model for predicting the tox really much earlier in drug discovery programs. The way it typically works is you get pretty far down the path to making your drug, then you do this toxicology testing. And unfortunately, sometimes you get a result that means you have to go all the way back to the beginning. What we're doing now with predictive tox is moving that ability to derisk your molecules higher up in the workflow.

And I think that there's broad excitement and interest in this because you can imagine the cost when you have to kill a molecule late -- and so that's something we're using in all of our collaborations and our internal programs. I think there's been beta testing going on and the results have come back significantly positive. And so really, it's now people incorporating that into their typical workflows. I don't know if, Pat, you want to speak about how that's going along.

Yes. I mean it's just taking -- one thing I'm especially excited about is I think we actually do have the customer spending on presenting that they had a program that failed because of the tox liability. A lot of these assays you run, theoretically, they compete with our predictive tox, but they don't tell you how it failed. It just comes back with a fuzzy red color or something like that. You say, oh, this is bad." -- and then you don't know how to design. And they tested every computational model available. None of them gave them any signal, and then they finally used predictive tox and told them exactly what was wrong with the molecule and they could design around it. And this was the first time they've ever been able to design around a liability like this.

Instead of just literally throwing darts against the wall is how it used to be. So we're super excited because as we've mentioned the multiparameter optimization earlier, the more prediction you can get inside that multiparameter optimization, the faster things can move.

Great. So in the last few minutes that we have, I do want to hit on 2 more topics. First is just collaboration and partnerships. And then the second last one is biotech monetization. So on collaborization, can you just talk about some of the therapeutic kind of partnerships that you have and some of the benefits that you bring to the table?

Yes, I can quickly start. I think Richie pointed out that these collaborations are very synergistic in terms of them learning how we use the platform at full scale. We have partnerships with BMS, Lilly, Novartis, lots of other companies as well, too many to list actually. One update that we do want to share is, obviously, a couple of years ago, we signed a pretty significant deal with Novartis. In the last couple of quarters, they've had some portfolio prioritization decisions, which means we'll be reshuffling the deck there a little bit in terms of the targets that are in that partnership. And I think Richie can comment on our view of that and how that changes the economics. We don't think there's a major impact, but that's something I think we'll be looking at in the third quarter. But generally speaking, collaboration demand continues to be really high, especially as people adopt all of our most recent technologies.

Great. And then I guess on the monetization front, you guys have created $750 million in kind of exits. I would love to think -- love to hear how you think about just longer term, the value creation opportunity, milestone payments and just kind of framing the biotech side.

Yes. Maybe just on the -- sorry, the $750 million, it's a collection of events. So there's upfront milestones, royalties and also the distribution of cash from when the biotechs are sold or the assets in the biotechs are sold. So it's really a combination of all of those things. As you know, Nimbus sold several assets, Morphic was acquired, Petra, Ajax. Those were all acquired as well. And so those distributions of cash are part of that $750 million. So sorry.

No, I was going to say the same thing, but now I lost track of the question.

I'll just add to wrap it up that BD, the monetization of a lot of our equity and also upfronts around transactions has funded the company since it went public. Morgan Stanley led the IPO in 2020. So that's been a funding source for the for Schrodinger. I think as we think about the path forward, we're focused on getting to profitability on an operating basis and continuing to develop more milestones, royalties, equity positions like Tectora that can provide further upside beyond a profitable stand-alone enterprise.

Great. Well, as we wrap up, Ramy, I'll leave it to you as we think about strategic priorities next 12 months, right? We talked about a lot of momentum in the business. What are you most focused on kind of next 12 months to execute on?

Yes. Always the first thing is, and I hope you can sense this is we're super excited about what we've accomplished in developing the platform. We're the leaders in this space. We are defining in some sense, the science that's required for designing molecules. So it's a huge priority, and we have a very significant effort as a company to continue to make that next breakthrough, and we're guided by the therapeutics group. We know what the challenges are, and we can always make things better. If it takes 2 years to get to a development candidate, well, that should be 1 year. And if there's a 1% failure, I'm exaggerating now slightly, it should be 0, right? We should constantly be trying to improve things.

So through the synergies between the drug discovery group using the software and the very large group that the scientific developers and the computer scientists working on the platform, we keep advancing the platform. Bunsen is an extremely important part now of our strategy over the next 12 months because we saw that the bottleneck for getting this sophisticated technology in the hands of our customers was lack of expertise, and we really think we've solved that with Bunsen. And then every time we release a new product, we don't do market research in the traditional sense. We don't go around asking our customers what do they want, right? We innovate and then we go and try and convince customers that they should be doing things differently.

And we've done that with predictive tox and with other solutions around biologics, around crystal structure prediction. So a big part of this is going out and talking to our customers and saying, you guys can do things more efficiently. Let us prove to you that it works and then help you get this deployed. And again, I think Bunsen will help a little bit with that. So that's another really important focus. And then the other thing, obviously, this goes without saying, but I'll say it anyway on Richie's behalf is that we're very serious about our goal of achieving breakeven profitability on an adjusted EBITDA basis by '28.

We think that's a really important part of the strategy. And we think we can accomplish that by continuing to grow. You hear about that. That's the primary focus is growing, but we can do it in this disciplined way, managing expenses and achieving that goal in '28. And so that's an important part of the strategy as well. Rich, was there anything that I missed?

All right. Well, I think we'll wrap it there. Thanks to the whole Shrodinger team here today. Appreciate your time.

Great.

Thank you.

Thank you. Thank you for standing by. Welcome to Schrodinger's conference call to review second quarter 2026 financial results. My name is Rob and I will be your operator for today's call. All lines have been placed on me to prevent any background noise. After the speaker's remarks, there will be a question and session. If you'd like to ask a question during this time, simply press star, then the number one on your telephone keypad. Please be advised that this call is being recorded at the company's request.

Now I would like to introduce your host for today's conference, Ms. Jaron Madden, Chief Corporate Affairs Officer and Head of Investor Relations. Please go ahead.

Thank you and good afternoon everyone. Welcome to today's call during which we will provide an update on the company and review our second quarter 2026 financial results. Earlier today, we issued the press release summarizing our financial results and progress across the company, which is available on our website at Schrodinger.com. During today's call, management will make statements that are forward-looking and made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, including, without limitation, statements related to our outlook for the full year 2026 and third quarter 2026, our plans to accelerate the growth of our and advance our therapeutics portfolio, the capabilities and potential advantages of Bunsen, our agentic AI co-scientist, the clinical potential and properties of our and our collaborators' compounds, the use of our cash resources, as well as our future expenses. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies, and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially due to a number of important factors, including the considerations described in the risk factors section and elsewhere in the filings we make with the SEC, including our Form 10-Q for the quarter ended June 30, 2026. These forward-looking statements represent our views only as of today, and we caution you that, except as required by law, we may not update them in the future, whether as a result of new information, future events, or otherwise.

Also included in today's call are certain non-GAAP financial measures. These non-GAAP financial measures are not prepared in accordance with generally accepted accounting principles and should be considered only in addition to, and not a substitute for or superior to, GAAP measures. Please refer to the tables at the end of our press release, which is available on our website, of these non-GAAP measures to the most directly comparable GAAP measures. This afternoon, Rami Farid, our CEO, will review our recent progress. Then Richie Jain, Chief Financial Officer, will discuss our financial results and 2026 guidance. Karen Akin-Sania, President, Head of Therapeutics, R&D, and Chief Strategy, and the Office of the Energy Officer Partnerships will provide an update on our Therapeutics portfolio. Pat Lorton, our Chief Technology and Chief Operating Officer, will join us for the Q&A.

And with that, I will turn the call over to Rami.

Thanks, Jaron, and thank you everyone for joining us today. We're very pleased with our momentum across the business in the second quarter. The biopharma industry is increasingly recognizing that a computationally driven predict first approach is a critical driver for accelerating drug discovery timelines and improving probabilities of success. Our ACV Growth of 27%, driven by broad-based demand, reflects this ongoing shift. In the quarter, we saw continued scale-up within large customers, uptake of new products, an improved biotech sector, and new customers across life sciences and material science. A key driver of our growth strategy is introducing products that expand platform usage and provide access to new budgets. We are excited about Bunsen, our AI co-scientist, which we officially launched in early access last week.

We have optimized Bunsen to execute our validated computational methods and leverage our decades of molecular discovery expertise by executing complex multi-step workflows, Bunsen helps expert computational chemists work more efficiently and run more design projects. Johnson will also enable, more broadly, drug hunters to access our software and run advanced simulations expanding our user base over time. Our longstanding collaborators, NVIDIA and Google Cloud, are providing additional tools and compute resources to support early access to Bunsen. Our throughput-based licensing model ensures we are positioned to capture the value of the significantly increased platform usage we expect Bunsen to drive. We are very pleased that Bristol Myers Squibb, a longtime customer and collaborator, is deploying Bunsen and expanding their use of the platform across their research organization. Our new strategic software agreement with BMS, announced earlier today, allows us to deploy Bunsen in conjunction with our computational technologies designed to enable large-scale chemical exploration. We view BMS's implementation as a compelling model for how large-scale deployment can expand platform usage and integration across the industry.

Our commitment to scientific innovation is a key component of our strategy to expand our addressable market and access new budgets. Our predictive toxicology solution is a clear example of this approach. Our advanced physics-based technologies predict off-target binding risks before synthesis, enabling customers to address safety losses liabilities early in discovery programs and deliver optimized candidates with substantial time and cost savings. Commercial evaluations are going well, and Predictive Talks has already contributed to our 2026 ACV. As the promise of AI dominates the dialogue in both drug discovery and materials design, I'd like to take a moment to discuss what differentiates Schrodinger's platform. For molecular discovery, AI is a powerful tool enabled by the data used to train it. The more high quality data, the more predictive the models.

By using rigorous first principles physics, we generate the accurate, reliable data required for AI models. This integration of the most advanced ground truth physics calculations and cutting edge AI is the foundation of our computational molecular discovery platform. By continually expanding our platform, platform capabilities and introducing new products, we are unlocking new budgets and broadening our user base across the biopharma and material science industries. None of this progress would be possible without our team, and I want to thank our employees for their dedication and hard work in advancing our mission. We are executing well against our strategy, expanding our software footprint, and continuing to build long-term value across our therapeutics portfolio. I'll now turn the call over to Richie to review our financial results. Thank you, Rami, and good afternoon.

In the second quarter, we saw strong execution across both our software and drug discovery businesses.

by robust ACV growth, a rapid acceleration in hosted revenue percentage, continued portfolio progress, disciplined expense management, and a strong balance sheet. Second quarter ACV was $29.6 million, which represents 27% growth year over year. The ACV excluding contribution was $22.6 million, a 23% increase compared to Q2 2025, and at the upper end of our expectations. On a trailing four-quarter basis, ACV reached $208 million, and first half 2026 ACV of $58.0 million represents 19% growth compared to the first half of last year. ACV growth was broad-based, driven by our top 20 pharma customers, as well as from biotech and materials science customers. Contribution ACV was $7 million in the second quarter, $5 million as a result of the Gates Foundation extending its funding for our Predictive Toxicology Initiative, based on the progress we've made, and $2 million from Gates Ventures in support of our continued work in battery research to develop and apply atomistic simulation methods to improve battery. performance. Total revenue for the second quarter of 2026 was 58.9 million.

Software revenue was 32.5 million, of which hosted revenue contributed 15.2 million, or 47% of the software total, compared to 31% in the second quarter of 2025. On October trailing four-quarter basis, hosted revenue increased to 30% of the software total from 23% in Q2 2025 and 27% last quarter. Overall, we are pleased with the progress we are making with transitioning customers to hosted licensing. Our year-over-year revenue growth continues to be impacted by our planned accelerated transition to hosted licenses, for which revenue is recognized ratably over the life of the contract rather than mostly upfront. We are pleased with the conversion dynamics we are seeing so far, and our priority remains converting customers as their contracts come up for renewal. As a reminder, increasing the percentage of revenue from hosted contracts will have a temporary negative impact on revenue, with every 1% increase in hosted revenue resulting in a 2-3 million reduction in reported revenue, depending on renewal quarter and contract length. Software gross margin was 71% for the quarter compared to 76% in Q2 2025, reflecting our planned accelerated transition to hosted software licensing.

Contribution revenue was $3.4 million for the period compared to $4.8 million in Q2 2025. The decline is driven by by the completion of the initial funding by the Gates Foundation in support of our predictive toxicology initiative, partially offset by the Gates Ventures grant supporting our battery research. Product discovery revenue was $23 million compared to $13.9 million in Q2 2025. The increase is primarily due to the receipt of a $10 million collaboration milestone from Ajax Therapeutics. Total operating expenses for Q2 were $74 million, a decrease of 6% compared to $79 million in Q2 2025. The decrease was primarily driven by lower headcount, CRO, and professional services fees, and reflects our commitment to disciplined expense management. All other income was $48.9 million, primarily due to a gain associated with the completion of Eli Lilly's acquisition of Ajax.

Net income for the quarter was $6 million compared to a net loss of $43 million in the second quarter of 2025. We ended the quarter with a strong balance sheet of $419 million in cash and marketable securities. The fully diluted share count was $75.8 million. According to our full year 2026 guidance, we continue to expect ACV to be in the range of $218 to $228 million, representing 10 to 15% growth over 2025. As a reminder, Q4 is our largest quarter and typically greater than 50% of annual ACV. We remain pleased with how opportunities we expect to close this year are progressing. We now expect drug discovery revenue to be in the range of 65 to 75 million, compared to our prior expectation of 55 to 65 million, due to the recognition of a $10 million collaboration milestone payment from Ajax.

Our operating expenses are expected to be less than in 2025. Finally, For the third quarter of 2026, we expect ACV excluding contribution to be 41 to 45 million, compared to 38.3 million in Q3 2025, which included 2.2 million of contribution ACV. To wrap up, our performance in the second quarter reflects continued momentum across our business, backed by a strong balance sheet. Given the combination of robust ACV growth, disciplined expense management, and accelerating hosted software adoption, we remain very well positioned to execute on our strategic priorities.

I would now like to hand the call over to Karen. Thank you, Richie. The therapeutics team is experiencing a boost in productivity across our portfolio following the integration of our new agentic AI co-scientist, Bunsen. While Bunsen has clearly accelerated the day-to-day productivity of our computational chemists, its impact extends to the rest of our cross-functional drug hunting team. Bunsen is enabling target analysis and structural biology workflows as our structure-based programs begin, as well as the integrated analysis of DMPK, pharmacology and toxicology data during the mature stages of programs. These capabilities are helping our scientists drive rapid, data-driven decisions and to accelerate the predict, design, make, test and analyze cycle. Our new global collaboration with Simseer Pharmaceutical Group, announced in July, combines our complementary, predict-first design approach with Simseer's preclinical and clinical execution. This collaboration allows us to rapidly translate discovery breakthroughs into clinical stage opportunities consistent with our evolving strategy for our therapeutics portfolio.

We are eligible for development and commercial milestone payments, as well as tiered royalties on net sales. The growing track record for Therapeutics designed using our platform provides the ultimate validation of its impact. Since 2020, we have realized over $750 million from our therapeutics activities across collaborations, co-invented drugs, and co-founded companies including Nimbus, Morphic, Structure, and most recently Lilly's $2.3 billion acquisition of Ajax. In June, data presented at the European Hematology Association for AJ1-11095, the phase 1 type 2 JAK inhibitor, which was the most advanced asset in the AJAK's portfolio, provided initial clinical evidence that the characteristics of this selective molecule align with the desired differentiated target product profile. With multiple programs eligible for future milestones and royalties, our pipeline continues to represent the potential for substantial long-term value. We look forward to keeping you updated on our progress and thank our team and partners for the advances across the combined portfolio of important innovations for patients. I'll now turn the call back to Rami.

Thank you, Karen. As you've heard today, the first half of 2026 has been marked by innovation.

innovation, and strong execution. From robust ACV growth, to the launch of our AI co-scientist Bunsen, to the continued advancement of co-invented development candidates now in the clinic, we are fundamentally transforming how drugs and materials are discovered. At this time, we are happy to take your questions.

Thank you. We will now begin the question and answer session. If you would like to ask a question, please press star 1 in your telephone keypad. If you would like to withdraw your question, again press star 1. Your first question today comes from the line of Michael Riskin from Bank of America. Your line is open.

Hi, this is Alexa Chan on for Mike. Thank you so much for taking your questions. I have a couple here. Maybe to start, can you give a bit more color on end market health in BioPharma and biotech, as well as customer activity overall? Kind of sounds like funding is moving in the right direction. And then for my follow-up, can you talk about how much maybe predictive tox has contributed to ACV so far and what are the expectations for that going forward?.

Sure. I'll take the first question you were asking, I think about the health of the biotech sector. We've noticed what I think a lot of other people have noticed, that certainly things are better this year than they were last year. I think a good reflection of that is the number of IPOs that we've seen this year compared to last year. I think we're already at two times where we were last year. And we're also noticing a trend, a better situation with regard to customers just in the biotech sector, where last year we saw what I think a lot of people were seeing is a number of biotech companies struggling to raise funds. And that impacted our business as it did a lot of companies that were selling technology to biotech companies. And we're just not seeing that. this year at anywhere near the same rate as we were seeing last year.

So these are all encouraging signs that I think are consistent with what a lot of other people are reporting with regard to predictive tax. We're not disclosing, of course, we haven't broken down the ACV from individual products, but it has contributed, as we said, to ACV this year. And it's part of our guidance that we provided for the full year. Rich, is there anything else to add to that? No. Great. Thank you.

Thank you so much. Thank you. Our next question comes from a line of Matt Hewitt from Craig Hallam Capital Group. Your line is open.

Good afternoon. Thanks for taking the questions. Maybe first up on Bunsen, obviously, congratulations on getting that first collaboration or contract signed. I'm just curious what the pipeline looks like and if you could provide any details on maybe what the pricing model for that looks like. Is it similar to the rest of the software or? Is there some other angles with Bunsen? I'll start and maybe hand it over to Pat. Yes.

We have, as we said, we're, well, thank you, first of all. Yes, we're excited, of course, about the agreement with BMS. They're one of our large customers, and as a result of the agreement of the collaboration, they significantly scaled up their access to our technology. And I think that answers sort of the second part of your question is that that's how we see Bunsen or how we think we'll recognize the value of Bunsen and translate all the excitement around the gentrification of these complex workflows flows is through the increased demand of our technology. And again, that's sort of what was borne out in the collaboration with EMS. Has anything else to add to that or? Pretty much. Yes. Yes.

And then maybe as a follow-up, obviously some nice progress on converting customers to hosted. And I recognize that probably a big chunk of that's going to come in Q4 when contracts renew. But as you're having those discussions now, what is the feedback? Is your sense that you could get a...

a lion's share of customers converted, say within the first two years, or is this going to maybe take a little bit longer than that? Thank you. Rich, you will cover that. Thanks Matt. So overall, we're still tracking towards the same goals of 75% hosted by the end of 2028. So far, we've been pleased with how we're progressing. We were 47% hosted this quarter. So I'd say we're on track towards our goals. As a reminder, we did transition a few customers prior to their renewal date, which did have an outsized impact on the hosted revenue percentage for this quarter.

But overall, the customer engagement has been really strong and we're progressing towards our three-year goals.

That's great, thank you. Your next question comes from the line of Scott Schoenhass from KeyBank. Your line is open.

Hey, team. Thanks for taking my question and congrats on the results. So you mentioned that Predictive Talks is now being included in ACV, and I know part of your growth strategy. was to unlock more pharma budgets by expanding product scope and capabilities. How much are we seeing in terms of ACV growth as a function of new product launches? And how should we think about areas for the next wave of product launches to unlock more of the budget? Thanks. Yep. Yes, no, that's correct. We, a major, I think,.

sort of contributor to growth so far in the first half of the year and our expectations are the same for the second half of the year is from new products as we said predictive talks is is not contributing to that growth so is so are other new products like retro synth in fact that's we mentioned that in the the bms collaboration that's a part of that agreement. So it's a major part of our strategy. As you know, we have a very productive R&D group at Schrodinger that continues to make scientific breakthroughs that result in these sort of exciting new products that continue to solve of problems that our internal therapeutic group encounters and the projects we're working on. And also the many thousands of users that we interact with. We accumulate all of that feedback. We learn about what sorts of technologies are required to have a bigger impact and we invest in those in those technologies and develop new products and they continue to play an important role in contributing to the growth. In addition to not just new products, but of course, customers, existing customers scaling up their usage of those products is also a contributor.

But new products are certainly a big part of it.

Thanks. And as a follow-up, maybe this is for Richie. You noted, you guys noted reduction in operating expenses. Maybe talk about the areas where you're able to gain efficiencies. It sounds like you're applying Bunsen internally to drive some efficiencies on the drug discovery side. But maybe some other areas you could talk about on how you're controlling costs here.

Yes, and thanks for the second for the follow up question. Yes, in addition to just enhance productivity inside the company, property expenses are down 6% year over year and also sequentially. We've seen some reductions in personnel costs as well as CRO costs and professional services. So this is a reflection of just the plans that we've put forward and the execution and that stuff.

Thanks, guys. Thank you. Again, if you'd like to ask a question, press star, then the number one on your telephone keypad. Your next question comes from a line of Evan Seegerman from BMO Capital Markets. Your line is open.

Hi there, this is Connor on for Evan. Thanks for taking our question. I have one for Karen. You noted in your prepared remarks today that your therapeutics team is already seeing productivity increases from the integration of Bunsen. as Schrodinger is on the cutting edge of computer-aided drug design, I'm wondering if you can just speak to the specific areas of your workflows where you're seeing the greatest benefits from the technology as we all kind of try to wrap our minds around maybe what the ultimate benefits of sort of AI and computer-aided drug discovery could be. Thank you.

Yes, happy to answer that. So as you know, we participate in structure based drug design and there's an explosion now of structures available because of a lot of what's going on broadly in the industry. And I think this is a great lift for Schrodinger in particular, our teams are now not just using to help us with running Schrodinger's platform, running the workflows computational chemists, but analyzing structures, analyzing binding sites. A lot of the products that we use are not just about predicting the properties of molecules, but they're also about characterizing, refining and accurately accurately sharing those structures for use by our platform. And I would say that that work has been accelerated. Those workflows have been accelerated by the use of Bunsen, which is now sort of trained or expert in running our workflows. So we're seeing that on the preparation of programs for structure-based drug design, but we're also seeing it at every stage of the drug discovery process, which includes chemistry, but it also includes a lot of ancillary functions who interface with the platform. So, yes, just pretty excited about what we're seeing.

And I think this is going to be, as you heard today, adopted by our peers in the industry. Maybe I can add to, I think, this concept.

Thank you. If I can just add, this concept of a co-scientist isn't just the sort of word that we throw around. In every sense of that word, Bunsen acts as a co-scientist in that allows users to initiate a number of tasks and projects in parallel and do it. a number of things at the same time. I'm just like, you could imagine a co-scientist. So we like to think of sort of, you know, every computational chemist, drug hunter in the group, all of a sudden has a team of people that are helping them perform some of the routine tasks that end up taking up a lot of human time. routine tasks being performed by Bunsen is also, in addition to what Karen said, is contributing to this efficiency and productivity.

Yes, I'd also add one thing that's really important is that, as everyone knows, AI doesn't sleep, but a lot of our users do. And one common problem in this space, both nights and weekends, you'll set up a bolus of work and you'll kick it off using your very expensive supercomputer. And one hour in, something goes wrong and it dies. Right. And now you've lost that night's work or that weekend's work unless you're actively monitoring it. But if you're working with Bunsen, it will see that the job has died. It will try to figure out the problem and it will be able to restart it. And the throughput of work you're able to accomplish just from that optimization, ignoring all the other. benefits of working with a co-scientist is really incredible.

Thank you. Thank you. Your next question comes from a line of Brendan Smith from TD Cowan. Your line is open.

Great. Thanks for taking the questions, guys. Maybe just one more follow-up on predictive talks. I appreciate all the color there so far. I think I wanted to ask maybe how we should think about kind of the 10 to 15 percent, you know, medium-term growth expectations you've noted previously. I guess, does that kind of factor in some assumptions about predictive talks already and maybe Similarly, based on your broader market and feedback in the launch so far, how are you and predictive talks relative to the kind of the core software business, just as some of this starts to get its legs over the next couple of quarters. Thanks.

Yes, you broke up a little bit, but I think we got the gist of your question. I think you're just asking for a little bit more color about how predictive toss, maybe more specifically, is contributing to growth. As we said, we're not breaking down sort of the component of growth that's... tied to predictive talks. But all we can say is that the evaluations are going very well. There's real demand for this kind of technology. A major source of failure are so many drug discovery projects is issues with safety. And safety is often tied to selectors activity and binding to off targets.

And that's what predictive tox aims to do. So we're pleased with the discussion so far. We're happy about how the evaluations are going. We're happy that it's already started to contribute. But as with technology like this, We're very used to introducing new technologies that are sort of transformative. There's a long lead time. It takes time to introduce the technology for customers to evaluate the technology, get their own results, which involves, by the way, running calculations, but also then validate those with experiment and that obviously takes time. So we expect to continue, for predictive talks to continue to contribute to growth for quite a number of years.

Got it. Thanks, guys. Appreciate it. Great. Thanks.

I am showing no further questions at this time. That concludes today's call. You may now disconnect.

This live transcript is auto-generated without human intervention or review.

[Call has ended.]

We'll kick things off. Thanks, everyone, for joining us. My name is Mike Ryskin. I'm on the Bank of America Life Science Tools and Diagnostics team, and I'm excited to host for our next session, Schrödinger. We're joined by Ramy Farid, Chief Executive Officer; Richie Jain, Chief Financial Officer; and Pat Lorton, EVP, CTO and COO of Software. Ramy, Richie, Pat, thank you so much for being here. Thanks for taking the time.

Thanks so much for having us. Looking forward to the discussion.

So the format will be a fireside chat with some Q&A. If you've got a burning question, raise your hand, and we'll incorporate you in. But maybe just to kick things off, Ramy, maybe one for you. Just you recently reported 1Q earnings, reiterated your full year guide. What are the key takeaways from the quarter, how it played out relative to your expectations? There's been a lot of moving pieces in the model the last couple of months. So let's talk through it a little bit.

Yes. I mean, of course, the performance itself was -- we were very pleased. We actually slightly exceeded the guidance that we gave. But I think beyond that, I mean, obviously, that's good. But beyond that, I think how broad the source of growth was. We saw existing customers, large customers scaling up their usage. We saw companies adding new products. That's always an exciting thing. When you introduce a new product into -- out there in the world, it's not a given that there'll be acceptance of it. So that was good.

And then the other thing, which is encouraging, I think, maybe for all of us is that unlike last year, where we certainly saw quite a few companies losing their funding, and we were losing customers because they were just disappearing. We saw the opposite this quarter. That's encouraging. Now we're all hearing kind of things seem to be getting better, but it's nice to see some data to support it. We actually added new customers, new logos. And it sort of feels like we haven't seen that in a little while. So the broadness, I think, of the growth and the quality of the discussions, the interest in the platform and then again, particularly the interest in new products was quite encouraging. Is there anything else I think we should add...

I'd just add as we drive towards profitability over a 3-year time period, first quarter out on that path, but increasing revenue across software and drug discovery while maintaining expense discipline. OpEx was down 4% quarter-over-quarter. So just the start of that journey.

Yes. So pretty encouraging start to the year.

And then in terms of the 2Q guide and sort of the peak through the year, there's always -- you guys have some pretty seasonal quarters. Nothing unusual to call out there, sort of in line with historical trends and your prior expectations, right?

That's right. It's typically a smaller quarter -- well, not typically, always is. Most of our renewals are in Q4. But we're optimistic about the continued growth opportunities in Q2, and I think the guidance reflects that.

And just Q1, Q2, we're out of the gate strong on hosted conversion as well. Hosted revenue as a percentage of total software revenue was 34% for the quarter compared to 24% a year ago. So we're pleased with that performance out of the gate there.

Good. On the 2Q guide, when you're talking about ACV, you had a slide in your deck where you did a sort of a year-over-year comparison and you had sort of -- you have to adjust the last year comp. Could you just walk us through that, remind sort of what the adjustment is and why the year-over-year is what it should be?

Yes. We guided $19 million to $23 million of ACV for the quarter. A year ago it was $23 million total, of which $5 million was contribution to ACV related to the grant funding we have from the Gates Foundation on predictive toxicology. So on an apples-to-apples basis, the year ago comparison is really $18 million and our guidance is $19 million to $23 million compared to that. So 4% to 27% growth.

And in terms of 1Q, you saw effectively de minimis ACV contribution from that. So...

Correct. There was no contribution from -- we called it out in Q2 just to make sure that you're looking at the comparisons the right way.

Yes. Okay. Okay. Maybe let's talk about that ACV transition and sort of the focus on ACV as the KPI of the business going forward. You made a big change in the model on that in the prior earnings call. It's been about 3 months now where you've sort of been talking to investors about it. What's been the feedback on that? How has that gone over because it is a pretty meaningful disruption to the model.

I think people get it. I think they understand that -- and because we're not the first company to undergo this transition, right, from on-prem to hosted. And in the year that you facilitate that transformation, the revenue has to go down. I mean it's math. And I think companies are used to -- investors are used to, okay, let's look at the thing that actually measures the business, which is ACV. And really, it's okay to ignore the revenue. It will catch up. In the meantime, focus on the ACV. And it seems like people are focusing on the ACV, right? I mean it's not -- that's good. And it's -- we're in a funny situation where we're supposed to be bragging about lower revenue, right? The lower the revenue, the better the hosting transition is. So I know that sounds weird.

But again, I think we're not the first company to be doing that. So we're happy about the lower revenue because it means that the transition to hosted is working. That's great. And of course, the faster you do the transition, the better so that you rip that Band-Aid off, you focus on ACV while you're doing that transition. And then soon enough, we'll be able to go back to revenue when most of the transitions occurred. So it's working. It's working quite well.

The other thing that's great is we're not meeting with that much resistance. We even had some companies we reported switching from on-prem to hosted before the contract renewal, that's a good sign. So a few years ago, that wasn't the case. I mean everybody was all worked up about hosted. What do you mean? You're going to have access to our data, but it's just the normal thing now. So right? I mean it doesn't seem like there's that much resistance.

Yes, there's virtually no resistance. It's really gone from 5 years ago, it was almost impossible to convince the top 25 pharma to do anything hosted. And now it's almost the opposite where the CIOs are coming and saying, you guys have a hosted offering.

It's easier for them, right? I mean we're taking care of the things that we're good at and they don't have to deal with it. So it all works out for everybody. Now we should be clear, there'll be some regions. I think we said in Asia, they're a little bit behind, so that might not happen, but that's okay. It's progressing where we expected it to, very, very nice, very well.

So I mean on that point, you said, as you called out, 34% hosted in 1Q, 24% a year ago, so a 10-point jump. I can do that math in my head, I hope. When you first announced this 3 months ago, I think you talked about 75% in 3 years, kind of give us a road map. You're not guiding quarter-to-quarter on what hosted should be because that's kind of ridiculous. But still, could you put 34% in the first quarter in context of that? Are you a little bit ahead of schedule? Is it too early to read into that?

Good question.

I'd say we're right on track. The journey from 23% at the end of '25 to 75% at the end of '28. We've said many times that we don't expect it to be linear. I think the first year is going to be a little bit slower on the transition as you measure it in revenue. Our actual progress in ACV will be much stronger, but the conversion of that to revenue in the first year is just reduced because it's the math part of the accounting. So I think year 1 will be slower, year 2 and 3 should be much faster on that path to 75%. So I think we're right where we expect to be.

And is 75% like a terminal limit? Is that just a 3-year goal? I mean, do you expect it to get to 90%, 95% to 100%...

75% was really set. I'll start and Patrick should add to this. That was set on our evaluation of the customer set at the time. There -- how they embrace hosted solutions, our view of the geographical differences and customer differences. As this evolves, as we move forward, we'll keep you updated on it, but that was our framing around 75%.

Material science customers, right, are a little bit more resistant than life science. It was a consideration of all of those things, yes.

Yes. And it's worth -- as Richie just said, it's worth mentioning that the 75% estimate comes from a moment in time. Had you taken that moment in time 3 years ago, we would not have been saying 75%. So it's hard to say what the ceiling will be in 3 years, but 75% is definitely a reasonable target today. We should add to that a lot of the new products that we're rolling out are hosted solutions. So again, that's another variable that we're thinking about on -- is 75% the endpoint or is there some different number.

Okay. And as you said earlier, as you get higher and higher up and as the change in hosted year-over-year diminishes, then the gap between ACV and revenue should shrink.

Exactly. It'll converge.

Yes. Okay. In terms of what -- where you've seen adoption pick up in the next 3 to 6 months, is it broad-based? Is it more big pharma? Is it a little bit of everything sort of...

It really -- that's what I was trying to say at the beginning. It really is -- this is a great sign. It's a little bit of everything. New small biotech companies, material science companies, certainly, large companies are contributing in the way you'd expect them to, but it's broad.

One thing really very exciting and we mentioned about how new products are contributing is if we close a new product sale with a big pharma, it's almost always off cycle because they have multiyear big bundled contracts. But what that does mean is we've now -- by adding new products, the next refresh is going to be an even bigger bundle. And we're successfully selling our new products into places off cycle, which is really exciting.

Yes. I mean something else, Ramy, you mentioned earlier was the new logos in the quarter. It seems just sort of a more constructive tone on end market dynamics. It's just one quarter. It's been very choppy there for a while, but we track biotech funding, new biotech funding things like that. It certainly feels a little bit better. Does one quarter make a trend? How much more do you need to see until you really think...

And there's a lag, right? It's going to take time, but it's encouraging, at least, right? It's certainly better. If we just look at the analysis of the quarter, it's better than Q1. That's last year. That's a positive thing.

I think it's obviously worth saying too that the lack of a headwind is better. When companies are going out of business -- that's net negative, and we have to make up for it in other places. And just that reduction is very useful for us.

Yes. I mean any ideas on what's certainly spurring this? Is it the pickup in M&A? Is it newer technology, sort of what's driving...

Funding environment, right? I mean, private funding, that certainly seems to -- all the stats point to that. I don't think to the point we were saying earlier, probably increasing IPOs, we're not going to see that right away. That's going to take time, right, to recognize that. I think it's that. It's actually fun. And it's kind of what Pat is saying it's sort of the lack of shutdowns, right?

The ones who've made it through the winter are hardier.

Right. Exactly. That's a good way of putting it.

Okay. And once those customers -- I mean, it's still -- it's early, but once those customers get their hands on money, are they spending it? Or are they kind of hoarding it? Because we've seen this dynamic play out a couple of times in prior years where you'll get a quarter or 2 of good funding, but you don't really see that come out because they're worried that it will go away, so they're hoarding...

Okay. I think that's an exciting thing to think about because I think -- and let me finish the thought because it's going to start sounding off not good, but it's going to end well. I think they're hoarding it, but that's good. They should be. And what does that mean? That means they should be doing -- what they should be doing is investing in things that are actually more efficient and that actually work. And that's obviously computation. And they can see that. I mean look at the success of Ajax just recently. And that's like one of many.

So they look at that company and say, how did they accomplish that? They have 5 employees, right, and got to this huge valuation and this great exit. So I think the environment is still such that we need to all be efficient. It's not like all of a sudden, everybody is rolling in money. So you need to be doing things in a really efficient way. And obviously, using computation at scale is significantly more cost effective and has a higher success probability than doing drug discovery by trial and error.

And we're also very fortunate our track record of success with our collaborative partners as those companies go to the Nimbus's and the Morphic's and Ajax and all these companies go through exits, a lot of their employees end up reentering the life cycle of a biotech, and they have demonstrated success using our technology. And we see new companies essentially being seeded with these people whose success was demonstrated with our technology and who's the first person they call. They call us before they get their CRO because they know they can start getting results immediately in computation. So it's very promising. The ecosystem we've built from these successful biotechs is very promising.

You touched a couple of times during your earlier comments on new products from your end. I mean maybe let's start with predictive tox. Sort of what's been the uptake? What's been the feedback? What impact are you seeing on the rest of the portfolio?

Yes. As we said before, we were very pleased with the results of the method internally. We started to get this into the hands of other customers outside the company. And there's one very, very encouraging sort of development that we're pretty excited about. Our vision for this was the way tox screening is done, this is done generally very late in drug discovery, right? You make -- you try and optimize them since you're not able to predict those properties and you're not going to run some expensive animal tox study through the whole discovery. You do it towards the end when you think you're close. And that's a horrible way of doing things because, of course, you -- late failures are always bad, right? That's very expensive. And that's what's happening, right?

You put a molecule that you think is clean, it goes into a GLP tox study and all of a sudden, it doesn't work, and that's it. You have to go back to the drawing board. And usually, that's when the projects get killed. Our vision is to move prediction of toxicology way earlier in the process. And we're really encouraged that our customers that are starting to get access to this have recognized this. That's how they want to use it. They want to use it early in what's called MPO right, the multiparameter optimization part of drug discovery, which is the very early stages. You want to be designing in safe molecules from the beginning so you don't have these kinds of surprises.

Now the cool thing about that for us is that requires using the technology on a much larger scale than the way tox screening is used now, which is what we're just talking about, right, just a few molecules at the end. So if it's being used on that scale, obviously, that has a pretty big impact on the revenue that can be generated from it. So we're really encouraged that people are recognizing what we were hoping would happen is that this is getting moved up way earlier in discovery.

And it's probably worth reiterating, as we announced initially, we had a few targets, a handful of targets enabled at the initial release. Last year, we released even more. But this is part of the ongoing work at the grant, and we're getting more and more targets. And the broader the domains we cover, the more interest this is to people. because they might already know we're worried about this liability in the space. And unless we have that in our panel, predictive tox is interesting for that project yet. But virtually every day, we're adding more targets to the panel, and that's going to make it more and more interesting for every drug discovery project out there.

That's a great point. How broad is that database or knowledge that now?

Yes. So where...

What part of the portion of real...

No, it's a great question. So here's where we are. Internally, we've enabled approximately 100 targets. It's around 65 kinases and then the remaining are other very important, well-known targets that you want to avoid that you don't want to hit that are associated -- that are clearly associated with toxicity. It's on that order of magnitude. As Pat said, it's true. We are adding them on a very regular basis, almost on a daily basis. What's the end goal? It's probably a couple of hundred targets, I think, is -- would cover a very significant portion of the -- as some people now are referring to it as the AVOID-OME, the target you want to avoid, the AVOID-OME.

And I think the fact that we've demonstrated that we have the infrastructure and the technology to enable these targets, it's become an engineering problem now, right? It isn't -- there's not a lot of uncertainty that we can get there. It's just a matter of enabling these targets, adding more targets to our tox panel, right? So that when you run it, you don't have any surprises that you're -- that there's a target -- an off-target that you're hitting, but that wasn't in our panel and we couldn't predict it.

So that's our goal, 150 to 200. But the good news is I think the targets that we've enabled right now is enough to really get companies intrigued and starting to use it in production, and we're really -- we're actually seeing that. So I think it's one of these things that will keep getting better, but it's good enough right now to get started.

Yes, absolutely, yes.

Okay. When I think about the Schrödinger platform, it is a platform. It is a number of solutions. We rarely go sort of like -- or almost never go product by product...

Do you want to do that now? Are we going to...

Line by line. No. But I mean we've talked about FEP+ in the past. Now we're talking about predictive tox. Is there anything else that really jumps out as sort of like no single item, I think, really moves the needle, but what are the other important...

Yes. So FEP+ is very important because that's a workhorse for predicting affinity of molecules to proteins, which is kind of one of the most important things. I mean, if your molecule isn't bind tightly to the target, it isn't going to work. So that's kind of a pretty important step. Now that technology, the same exact technology, the same physics that underlies that technology can be used to predict another property that's really important, which is solubility. Obviously, if a molecule is not soluble, it isn't going to be a drug. It's not going to be bioavailable. There's a solution for predicting permeability. Again, if the molecule can't get through the cell membrane, it's not a drug. It's not getting to the target. It's not going to be bioavailable. So those are areas that we're excited about.

The other really big area that we're focused on is what's required as input to these calculations. The input to these calculations is an actual structure of the protein. And it has to be an accurate structure. You got to get the atoms on the right place. Physics actually cares about where the atoms are and then they're in the right place. So there is a lot of work going into it, and we're making really, really cool progress here, really exciting progress on being able to determine the structure of a target to high resolution. That's enabling us to work on targets that were not possible to work on before. That's pretty important. And of course, it's the technology that enables the whole predictive tox initiative. So protein structure prediction, another really big area.

Now we're also investing in our enterprise informatics system. This is essentially the platform where a scientist comes in the morning and work and sees everything that's going on in the project. Where -- what molecules have been made overnight, what were the predictions, what are the experimental results? And what am I going to make next? So they design in that interface. They collaborate. They collaboratively design molecules in this platform we've developed called LiveDesign. And it's getting used very heavily in the industry, a very large number of our customers have it. But that is essentially the interface to all of this technology.

So it's fun to talk about all the science and these advances. But if you can't access the science, it isn't as valuable. So we put a big effort into making sure that the interface, the way you access these very sophisticated computational tools is also something that's highly robust, scalable, can deal with massive amounts of data, which we're now creating. So that's another area that we're really investing heavily in.

The other area, just to focus on, just for a moment, I won't spend too much time in it, but a lot of these physics-based methods that we're developing that are aimed at designing drugs are immediately transferable to designing materials. So we do have lots of exciting technologies around, for example, designing batteries. That's obviously a pretty important area, polymer coatings for every application from aerospace to food packaging and lots of other things. I don't want to spend too much time on it given the focus of this conference. But it is -- those are very important areas, and we're very excited about them.

And so now let's -- I'd love to highlight 2 of the big new things that we're focusing on. So Ramy talked about LiveDesign, and it is used at the overwhelming majority of top pharma as well as many, many biotechs, but it is used primarily for small molecule design right now. In the last year, we did our initial release of LiveDesign Biologics, which targets the other 50% of R&D out there. And we're seeing really great interest. We have several big pharma that have signed up for pilots. We have a couple of big pharma who have entered in co-development because they're going to integrate in their entire system. So we see this as an opportunity to extend from our base of small molecule strength into the biologics space where there's an equal sized R&D budget.

In addition, what we announced on our last earnings call, Bunsen is the new Agentic AI that we released. We are extremely excited about this. We've been using it internally since late last year. It has the ability to take a computational chemist who is completely overloaded and can only do a fraction of the work they'd like to do and give them a dramatic speed up similar to what we're seeing with cloud code and developers. We might have referenced it before, but internally, when we're running programs, the reason we have the success rate is we have something like 4 computational chemists per discovery program in the industry that -- it's the inverse of that. It's something like 1/2 to 1/4 for discovery program.

So we're putting 8x to 16x the computational resources, just human being wise, not even GPUs. We think Bunsen can help close that gap. We can take somebody who only has half of an FTE to put on a project and allow them to accomplish 2x to 5x as much and in the process, use 2x to 5x as much of a throughput-based licensing. So it fits perfectly with our commercial strategy and our scientific strategy. So we're super excited about Bunsen and LiveDesign Biologics.

A bunch of that I want to follow up on real quick. First, Ramy, your point on protein structure elucidation. Just to be clear, is that entirely computational? Or is there an experimental component -- we've used cryo-EM in the past.

Yes, that's a great question. There are many methods for producing low-resolution structures, which are an input to determining high-resolution structures. So experimental methods like X-ray crystallography, cryo-EM and then applications like AlphaFold. They all produce sort of a good starting point, but they're not the end result. Those structures are simply not accurate enough to use in sophisticated physics-based methods. So then there's another computational layer on top of that, that relies very heavily on rigorous physics-based methods to take those kind of low-resolution approximate structures and get them right. And so we're developing those technologies, the latter.

But of course, we will -- I mean, thank goodness for those other methods, right? Otherwise, there would be nothing to apply those sophisticated physics-based methods. So it's highly collaborative and you need all of that, the experimental methods, the low-resolution computational methods and then the high-resolution computational methods that we're developing.

Okay. Okay. And on Bunsen, yes, I mean, I think you just announced it a couple of weeks ago, I think early access this summer, right?

Yes. On track for that. Yes.

How do you expect the initial commercialization of that to go? Are you targeting specific accounts? Sort of what's that road map going to...

Yes. As always, when we initially release something like this, we go to our close partners, and we look for feedback. We need -- we -- of course, with all new technology, I mean everything, there'll be things that work really well and things that need to be improved. And it's nice to know what those things are, so we can prioritize them and continue to develop them. So we have a set of our partners that we generally work with. We've already demoed the product to some of those. They're blown away. So far, it seems like the reaction is universally positive. This is something they've been waiting for because they're struggling, right?

They're struggling. They know that if they use the technology on a larger scale, they're going to have a bigger impact. They're seeing that happening. They see that happen at Ajax. They see it happening at Schrödinger, and they want part of that. And I think they see this as an answer to that. And we'll get -- do the usual thing. We'll get that feedback. We'll incorporate that, have another release, and we'll do that in a pretty rapid pace. We're pretty excited about it.

One additional point I'd say that really does differentiate us from a lot of people in this space is we already have a couple of hundred drug discovery people using this. So we're -- because we have our own internal drug discovery and we can dog food with them, we're not going to release something that we think, oh, this might work, the customer feedback is a polishing phase. We already have computational chemists in-house that are telling us they are multiple times more efficient. We have medicinal chemists accessing technology that was impossible before. We know this works. It's really just getting into that final stage because there is a gap between an internal platform and a commercially sold platform. So we're really getting that final polished done to the point where there's a commercial quality platform.

Yes, good. I mean just on that point, just play devil's advocate a little bit, though. You're not the only ones launching some of these agentic solutions, right? Just in the last 3, 6 months, a lot of companies come out of the woodwork, both traditional tools vendors and more traditional AI or software vendors. I don't want to say that the competitive landscape is crowded, but again, it's not the only platform hitting the market. Do you have the name recognition? Do you have the relationships to sort of give you a leg up there? Have you been asked to do like head-to-head demos or sort of like what's that market look like?

So it's really interesting. We haven't been asked to do head-to-head demos, but we -- so I just actually demoed this to 14 of our top customers last week and got 100% interest. The -- we haven't asked to do head-to-head demos because when they've tested the generic solution so far, they just fall flat. They don't have the specialist knowledge they need. One thing we've been able to do is through constructing specialized harness and skills that know exactly how to use our software, exactly how computational chemists work, we can build something that a genericist just cannot do. There is not enough information on how this works out there. So our core advantage is kind of what it's always been.

We understand computational chemistry better than anyone else, and we're embedding that into the agent. That said, if one of these other agents work, the only way it's going to work is if it has access to our tools because the agent can't make up for the fact that the tool isn't accurate enough. So if another agent beats us at one of these customers and it's using our tools, we will still capture the throughput-based usage of our tools. That's still a win for us. We do think to do everything right, at least at this point from what we've seen, we have to own that because the skills of these agents not developed by us is just simply not there.

Okay. We've only got a couple of minutes left, still a couple of other topics I want to touch on. Maybe, Richie, a couple for you. You talked about profitability, operating leverage in the quarter, just sort of tightening up that cost control a little bit. Could you just talk about the ramp from where you are now towards EBITDA positive? And then maybe I'll just throw on it at the same time, Ajax, parts of the therapeutics collaboration portfolio and maybe some of the incoming cash flows. You haven't seen the cash come in yet, but how does that change your approach on where you're investing and how much?

Sure. I'll take those in reverse order. So we ended the quarter at $402 million of cash. We should expect that to increase once we receive our 6% portion of Ajax's upfront with Lilly. That amount was not disclosed, but that will be an increase in cash for us. In terms of the use of our cash, this is mostly investing in operating burn over the next few years and investing in R&D and science to support a growing software business and drug discovery business. So the extra use of cash, once we receive it, we'll evaluate what to do with it. I think we've said in many different forums. Our focus right now is investing in growth. If there are adjacent nearby adjacencies for M&A, we'll consider that as well.

The outlook over a 3-year time period is 10% to 15% growth in software ACV while transitioning over to hosted revenue and having the revenue converge with ACV. $150 million of revenue in drug discovery over 3 years. So roughly $50 million a year. The timing of that can be a little variable, but $150 million over the 3-year period. And this year, we're guiding to operating expenses being lower than last year and then a disciplined framework on that going forward. So the entire package there is what leads us to the profitability goal on adjusted EBITDA in a 3-year time period.

Okay. Okay. Maybe just with the last 30 seconds, Ramy, I'll toss it up to you. Any concluding remarks, any last points you want to leave us with? What's been some of the more interesting feedback you've got or maybe some questions you had with investors you'd like to address?

Sure. Yes. I think, first of all, obviously, what we were saying before, I think it is really worth pointing out, it's very encouraging to see that things are improving. We're happy with the sort of broad-based growth that we're seeing. One of the things that I also really want to highlight that I think sometimes gets lost, there's a lot of noise out there, right? There's a lot of companies talking about computation and AI. And I hope that investors recognize that these companies aren't all the same. And I think our track record, which is pretty extraordinary.

I mean, Ajax is just the latest example of that, but starting with Nimbus and Morphic and Relay and Structure and Petra, these -- and the nearly 100% retention rate of our customer base and the tens of thousands of users using the platform and even we were talking about earlier today, companies, biotech companies that are using the software on this really large scale, seeing these really great successes. This is really an actual track record. We're delivering development candidates. We're delivering molecules that are making it through the clinic. There's, by the way, nontrivial royalties on sales associated with those. So we look forward to realizing the value of those in the future.

And I hope there's a recognition that there's a track record here of delivering with science that actually works when it's used at scale, and that's differentiated from some of the other things that you might be seeing. I hope that doesn't sound arrogant and it's not -- I don't like to sound critical of what other people are doing, but I think it's worth mentioning because it's a pretty big difference. I hope that was okay to throw in there...

Yes. That's a good place to end it.

I appreciate the opportunity to say it. Thank you.

Of course, of course. All right. With that, thank you very much. Thanks, everyone. Appreciate it.

Thanks.

Thank you for standing by. Welcome to Schrodinger's conference call to review First Quarter 2026 Financial Results. My name is Rob, and I'll be your operator for today's call. [Operator Instructions] Please be advised that this call is being recorded at the company's request.

Now I would like to introduce your host for today's conference, Ms. Jaren Madden, Chief Corporate Affairs Officer and Head of Investor Relations. Please go ahead.

Thank you, and good afternoon, everyone. Welcome to today's call during which we will provide an update on the company and review our first quarter 2026 financial results. Earlier today, we issued a press release summarizing these results and progress across the company, which is available on our website at schrodinger.com.

During today's call, management will make statements that are forward-looking and made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, including, without limitation, statements related to our outlook for the full year 2026, our plans to accelerate the growth of our software business and advance our therapeutics portfolio, the clinical potential and properties of our and our collaborators' compounds, use of our cash resources as well as our future expenses.

These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially due to a number of important factors, including the considerations described in the Risk Factors section and elsewhere in the filings we make with the SEC, including our Form 10-Q for the quarter ended March 31, 2026.

These forward-looking statements represent our views only as of today, and we caution you that except as required by law, we may not update them in the future, whether as a result of new information, future events or otherwise. Also included in today's call are certain non-GAAP financial measures. These non-GAAP financial measures are not prepared in accordance with generally accepted accounting principles and should be considered only in addition to and not a substitute for or superior to GAAP measures.

Please refer to the tables at the end of our press release, which is available on our website for reconciliations of these non-GAAP measures to the most directly comparable GAAP measures.

This afternoon, Ramy Farid, our CEO, will review our recent progress. Then Richie Jain, Chief Financial Officer, will discuss our financial results and 2026 guidance. Karen Akinsanya, President, Head of Therapeutics R&D and Chief Strategy Officer, Partnerships, will review our therapeutics portfolio. Pat Lorton, our Chief Technology and Chief Operating Officer, will join us for the Q&A.

With that, I will turn the call over to Ramy.

Thanks, Jaren, and thank you, everyone, for joining us today. We are off to a strong start this year, delivering $28.4 million in ACV, a 12% increase compared to Q1 last year. Our growth was broad-based, reflecting usage scale-ups, new customers and growth from new products.

Drug discovery revenue of $23 million was also a significant contributor in the quarter. Lilly's announced $2.3 billion acquisition of Ajax Therapeutics, a company we co-founded and in which we have an approximately 6% equity stake is the latest example of a multibillion-dollar deal for a Schrodinger co-developed molecule and speaks to the power of our platform. We are pleased with our momentum transitioning customers to hosted licensing.

We are seeing positive conversion dynamics upon contract renewals and with new products that are hosted. In limited cases, we are also seeing the early conversion of multiyear on-premise deals to hosted ahead of the scheduled renewal date. We are encouraged by the improving biopharmaceutical funding environment. While macroeconomic uncertainty remains, it is clear to us that there is a growing recognition of the critical importance of our computational platform as R&D organizations embrace the predict-first computational paradigm that offers a demonstrated path toward improving probability of success and reducing the time and cost of molecular discovery.

We remain poised to benefit from the evolving regulatory environment with our predictive toxicology initiative set to address a key element of the FDA's focus on reducing animal testing and broadening the use of computational methods. Our market-leading position is built on the inherent accuracy and scalability of our physics-based approach and is further reinforced by our unmatched track record.

While standard AI models are limited by the scarcity of training data, our platform generates the ground truth simulations, accuracy and scale required for AI to precisely navigate the vastness of chemical space. By combining the accuracy of physics with the speed and scalability of AI, we are able to evaluate key properties of billions, even approaching trillions of molecules with a level of accuracy impossible to achieve through models trained solely on experimental data.

This capability enables our customers to integrate computation more deeply into their workflows, driving the consistent demand that underpins our long-term growth trajectory. We are committed to technology leadership and evolving our platform to meet customer needs. We are very excited about the upcoming release this summer of an early access version of Bunsen, our new agentic AI co-scientist. Designed to autonomously execute complex molecular discovery workflows, Bunsen enhances productivity and accelerates the design-predict-make-test-analyze cycle that drives modern molecular discovery.

Our material science and therapeutics teams have been successfully using Bunsen internally, and we are excited to offer this capability to our customers. Our throughput-based licensing model is well positioned to capture the value of this expanding utilization. The repeated success of our co-invented molecules and the continued progress of our therapeutics portfolio place us at the forefront of a digital transformation, moving material science and life science industries toward a more efficient, predict-first, computationally driven model of discovery. We continue to deliver the technology that transforms the way molecules are discovered, and we look forward to updating you on our progress throughout the year.

I'll now turn the call over to Richie.

Thank you, Ramy, and good afternoon. ACV in the first quarter was $28.4 million, which represents 12% growth compared to $25.4 million in Q1 2025. On a trailing 4-quarter basis, ACV reached $201 million. As a reminder, we believe ACV provides important visibility into the performance of our business during a period where we expect recognized revenue to be highly variable due to the accelerated transition to hosted. ACV growth was primarily driven by our top 20 pharma customers as these customers broaden their platform access, onboard new products and integrate our platform more deeply into their R&D organizations.

Starting this quarter, we are breaking out contribution revenue as a separate line item to provide better visibility into our software and drug discovery performance. To facilitate year-over-year comparisons, we have reclassified our historical results to reflect this change as contribution was previously included in software and drug discovery revenue. Total revenue for the first quarter of 2026 was $58.6 million. Software revenue was $35.6 million, of which hosted revenue contributed $12.1 million or 34% of the software total compared to 24% in the first quarter of 2025.

On a trailing 4-quarter basis, hosted revenue increased to 27% of the software total. As we've discussed, the year-over-year software revenue comparison reflects our planned accelerated transition to hosted licenses for which revenue is recognized ratably over the life of the contract rather than upfront. While this dynamic creates a near-term headwind on recognized revenue, over the long term, it will better align revenue with operational growth, resulting in a more predictable financial profile. Software gross margin was 69% for the quarter compared to 80% in Q1 2025, reflecting our planned accelerated transition to hosted software licensing.

Contribution revenue was $0.1 million for the period compared to $4.3 million in Q1 2025. The decline is driven by completion of the initial funding by the Gates Foundation in support of our predictive toxicology initiative. Drug discovery revenue was $22.9 million compared to $10.2 million in the same period last year. The increase is due to the accelerated recognition of deferred revenue associated with the continued progress of the company's collaboration portfolio and the discontinuation of one collaboration program.

Total operating expenses for Q1 were $78 million, a decrease of 4% compared to $82 million in Q1 2025. This reflects the impact of our efficiency measures and disciplined expense management across R&D and G&A, while we continue to invest in sales and marketing to drive long-term growth. Total other expenses were $11 million, primarily due to changes in fair value of equity investments and interest income expense. Net loss for the quarter and for the first quarter of 2025 was $60 million.

We ended the quarter with a strong balance sheet of $406 million in cash and marketable securities. We anticipate receiving our portion of the upfront cash payment from the Ajax Lilly transaction when the deal closes. The fully diluted share count was 74 million. Today, we are maintaining our full year 2026 guidance. For the full year, we continue to expect ACV to be in the range of $218 million to $228 million, representing 10% to 15% growth. We anticipate drug discovery revenue between $55 million and $65 million for the year. As a reminder, drug discovery revenue has quarterly variability due to the collaboration and milestone-driven nature of the business.

Our operating expenses are expected to be less than 2025 as we maintain overall expense discipline and make select investments in sales and marketing to support growth and the release of new products. We anticipate our clinical activities will be largely complete by the end of 2026 and to incur approximately $10 million to $15 million of R&D for full year 2026 as we wind down these activities and seek partners for mid- and late-stage clinical development. Our $19 million to $23 million guidance range for Q2 2026 ACV excludes contribution ACV compared to $23.3 million from Q2 2025 that included $5 million of contribution ACV.

Now I would like to hand the call over to Karen.

Thank you, Richie. Our therapeutics business continues to create significant value, most recently highlighted by Lilly's planned acquisition of Ajax Therapeutics for $2.3 billion. By combining Ajax's deep expertise in blood cancer and JAK family structural biology with our industry-leading track record in computational drug design, we discovered AJ1-11095, a first-in-class Type II JAK inhibitor, which is the primary driver of the announced deal.

Over a 10-year span, Schrodinger has cofounded multiple companies, including Ajax. There have been 7 major transactions and liquidity events related to molecules we co-discovered across our biotech collaboration portfolio, including Lilly's acquisitions of Morphic, Petra and Ajax, the sale of Nimbus' ACC and TYK2 inhibitors and the successful IPOs of Relay and Structure.

The success of these companies and multibillion-dollar exits establishes unquestionable validation of the impact of computational physics-based design and our biotech and pharma collaboration business model. The emerging results from our maturing therapeutics portfolio span internal discovery programs licensed to pharma through to co-invented molecules with late-stage clinical readouts like Takeda's zasocitinib, which completed Phase III trials earlier this year.

To date, our equity and business development activities have resulted in close to $700 million of cash as well as potential future preclinical, clinical and commercial milestones of up to $5 billion and royalties on 15 programs. Our wholly-owned programs also represent future value capture opportunities. As Ramy mentioned, the therapeutics team has integrated our new agentic solution, Bunsen, across the combined portfolio. Bunsen's ability to execute our powerful predictive models and orchestrate multistep, multiskill drug discovery workflows enables us to accelerate the design-predict-make-test-analyze cycle. This is an exciting development that we expect to have a major impact on the productivity of our team and teams across biopharma once they get access.

Turning to our wholly-owned portfolio. In April, we presented initial clinical data for SGR-3515, our Wee1/Myt1 inhibitor at the AACR Annual Meeting. As a reminder, this is an ongoing Phase I dose escalation study with primary objectives of safety, tolerability and pharmacokinetics. The data presented demonstrate that SGR-3515 was generally well tolerated on an intermittent dosing schedule of 3 days on and 11 days off. Importantly, the initial clinical biomarker data validated our hypothesis that dual inhibition can overcome compensatory resistance mechanisms.

We observed encouraging early antitumor activity with a 65% disease control rate among evaluable patients treated at doses of 100 milligrams or higher. We also remain encouraged by the progress of SGR-1505, our MALT1 inhibitor. We continue to see a 100% response rate and durable responses in patients with Waldenstrom's macroglobulinemia, where the drug has both FDA fast track and orphan drug designations. As we complete these Phase I studies, we are actively exploring partnership opportunities to continue the mid- and late-stage development of SGR-1505 and SGR-3515.

Our track record of generating differentiated discovery stage breakthroughs, clinic-ready molecules and valuable data packages is well established. We believe our drug discovery expertise, coupled with the use of our computational platform at scale will enable us to continue unlocking high potential target product profiles and drive the next wave of successful collaborations and transactions.

I'll now turn the call back to Ramy.

Thank you, Karen. As you have heard, we are off to a strong start in 2026. I want to thank our employees for their hard work and commitment to our mission. We are pleased with the momentum across the company and look forward to updating you on our progress throughout the year. At this time, we are happy to take your questions.

[Operator Instructions] Your first question comes from the line of Scott Schoenhaus from KeyBanc Capital Markets.

This is Steve on for Scott. Could you talk more about how agentic AI is driving high utilization of high compute calculations and how this is impacting your business? What's the upside potential as adoption [ of AI ] increases? And then how will this show up in your customer contracts?

Absolutely. Yes. I assume you're referring to the announcement we just made about the release this summer of Bunsen, an agentic AI system for automating complex workflows. We've already been using Bunsen internally for a number of months. The impact that it's had already on productivity of both our expert modelers and computational chemists as well as nonexperts has been extraordinary. We're very excited about it. What it's doing is eliminating sort of barriers to large-scale deployment of the technology.

And it's very much, as we describe it, a co-scientist, a companion that improves efficiency and productivity, again, both for experts and nonexperts. And our collaborators who we're working with are already recognizing the impact, this improved efficiency and our ability to actually use the technology on a larger scale and in a more effective way. Again, as we said, we'll be releasing it this summer.

Already feedback that we've been getting as we start to talk about the imminent release of Bunsen has been very, very positive. I think there's a lot of excitement about the potential. The last thing I'll say is -- and we've talked about this actually in the last earnings call, and we mentioned it again today that our throughput-based licensing that is not seat-based licensing, but throughput-based licensing, of course, benefits from solutions like this, where agentic AI has the potential to increase the demand for the technology and the need for customers to license the technology on a larger scale. Pat, is there anything you wanted to add? Did I cover it?

I think you pretty much covered it. I think the one thing I would add is that we are seeing customers using more general generic agentic AI, and they are already having access to higher throughput of our technology using other LLM providers.

That said, the reason we've built Bunsen is because our tools are such an expert tool that we feel that the LLM has to be trained specifically to how to use our tools to optimize it and to run in the most efficient way. And we think the solution we're putting together will be best for that.

Yes.

Great. And then just one follow-up. You mentioned you're working with Anthropic last quarter. Just any update on that partnership or collaboration, how you're going to refer to it?

Sure. Pat, do you want to give an update?

Sure. Yes. We're -- we regularly work with and talk with Anthropic as we're building out Bunsen. It is one of the top LLM providers. We are not tied to a single LLM. We are open to using whatever our customers prefer or whatever we think would be working best. We're building an agentic layer on top of LLMs, but Anthropic is obviously a fantastic provider in the space, and we're -- we've learned a lot from them, and we're really excited to continue to work with them.

Your next question comes from the line of Mani Foroohar from Leerink Partners.

A quick question. When you think about the percentage of customers or percentage of contract value that are previously on-prem that are renewing in 1Q, recognizing that we're often recycled for many. Can you give a sense on what percentage we're able to convert over to hosted? Just if you can give us a little bit of real-time quantitative feedback on how that transition is going?

Yes. Richie?

Yes, I'll cover that. Thanks, Mani, for the question. For the quarter, we were pleased with the progress for the revenue -- hosted revenue was 34% of the software revenue in the quarter and 27% on a trailing 4-quarter basis, that compares to 23% just a quarter ago. So we're pleased with the early progress.

Anecdotally, we're aiming to transition from on-prem to hosted upon the contract date. So that's what we achieved in the quarter as well as all new customers were deploying them hosted in the first instance. So overall, we're pleased with the first quarter and still have our same expectations for the year and the 3-year outlook, getting to 75% by the 3-year period.

I think it's also worth mentioning that in a few cases, which I think is quite encouraging is that we were able to transition some customers to hosted before their renewal dates. Richie, do you want to add there? Is it worth...

Yes. While the primary emphasis has been on renewing -- sorry, transitioning at renewal, in a few instances for larger multiple year contracts that were on-premise, we were able to work with those customers and transition over to hosted well in advance of the renewal date. So that you'll start, there was modest impact of that in Q1, but you'll start to see more impact on that in Q2 onwards.

Great. And a quick follow-up. We're seeing a substantial pickup in M&A activity in private biotech markets, Ajax being one example. How much velocity would you have to see in that space to start tinkering with how you think about guidance for drug discovery revenue given the broad portfolio of co-founded, partnered, et cetera, companies and your equity exposure there?

Yes. I mean, first of all, we can certainly -- and Karen, I'll hand it over to you to answer the -- to answer. But let me just say, on the software side, we're also quite encouraged. We definitely -- things look a lot better this year or this quarter, I should say, so far this year compared to last year, where we saw lots of biotech companies, of course, shutting down or very significantly reducing their discovery budgets. We're not seeing that. We're even seeing a pickup in new customers. So that's very encouraging and that sort of dynamic that you -- is the premise of your question is certainly impacting, we think, the software business. As far as the drug discovery business, Karen, I think if you have some thoughts about that?

Yes, sure. Happy to share. So as you know, we have always had a lot of interest in partnerships, both obviously with the companies we've co-founded. You mentioned Ajax and prior companies we've co-founded. But I will say that your comment about the private market and companies who are still in stealth even as well as public companies still reaching out very actively to Schrodinger with respect to collaboration on programs that are in their pipeline, but also on new programs. And so we remain very enthusiastic about the potential for new collaborations. Obviously, we're not guiding to any specific BD event, but the momentum and the interactions remain very robust, both with biotech and with pharma.

Your next question comes from the line of Brendan Smith from TD Cowen.

Great. Congrats on all the progress here. Actually, I wanted to first quickly ask about the predictive tox launch. If you can maybe just give us a sense of, if not relative, revenue breakdown between the legacy business and that -- then predictive tox, at least maybe how new customer adds there are tracking?

And then just quickly, I guess, on the upcoming Bunsen launch, how should we really think about this go-to-market strategy to the agent? I know you gave us some good color earlier. I guess, is this something that you expect to kind of roll out as an add-in with existing customers? Or is there kind of a whole separate base you could potentially reach with this? I guess just any kind of go-to-market strategy there, stuff would be super helpful.

Yes, we can cover both of those. Thanks for the questions. With regard to predictive tox, feedback continues to be really very, very positive for the results of evaluations now that are being kicked off. It's very clear that there is significant interest in the technology, but also that prospective testing of it in our customers' hands is validating the kind of results that we were seeing when we were developing the technology and using it also prospectively internally.

So that's really quite gratifying to see. So that continues to go well. With regard to Bunsen and the go-to-market strategy, and you asked about the base, that's a really good question because -- and I sort of alluded to this in talking about it earlier. Certainly, this, in some sense, democratizes access to very sophisticated technology. And you can appreciate what kind of impact that can have on the business.

So when this kind of technology previous to systems like this may just have been inaccessible and it would take years of training, you need an advanced degree, you're not sure or you're using the technology and not using it quite right and not getting very good results. That's not a good thing for anybody, for the customer or for us. So this obviously very directly addresses that. This is very similar to image processing. That used to be a thing that was not available to very many people, only people who were expert users at Photoshop, right?

And to remove red eye or remove somebody who is in the background of your vacation photo was really difficult. Now you just circle the area and say, remove the background, and it's done. It's the same basic idea. And now all of a sudden, very sophisticated image processing or image manipulation is available to the masses. We expect the same sort of thing -- well, not masses, but you understand to nonexperts in research. Pat, anything to add to that?

No, I think that sums it up great. And the other thing I would just highlight on top of adding additional customers is one thing that is really limiting. I think we've discussed in the past, the amount of computational chemists we have per project at Schrodinger is a lot higher than the industry average. That's part of the reason behind our very high success rate.

One thing that's very limiting in our customers is they simply don't have enough people who can run this to get it done even if they have experts who are good enough. So simply getting this in the hands of those experts and allowing them to get a multiple of their work done, similar to how the agentic coding tools have allowed developers to work much, much faster. We think even those experts being able to run much, much faster, they'll be able to consume a lot more of our throughput-based licensing before we even have it broadened to a broader user base.

Exactly.

Your next question comes from the line of Michael Ryskin from Bank of America.

First, I want to dig into sort of the new way you're guiding ACV. I want to talk about the contribution ACV. So you called out for the second quarter, your guide is $19 million to $23 million, and that's excluding any contribution. So is that just -- is that your way of saying we don't know what the contribution ACV will be? Or are you actually expecting it to be 0 because it was relatively modest in the first quarter? And sort of the same question for the full year. Anything you could tell us in terms of how much of the full year ACV is made up of that or how much that was in all of 2025?

Yes, Richie?

Yes. So the guidance for the Q2 is $19 million to $23 million, as you noted. The reason we explicitly called out the comparison to last year, Q2 of 2025 was $23.3 million, of which $5 million was contribution ACV related to our grant with the Gates Foundation. So we just wanted to call out in the -- when you're looking quarter-over-quarter that on a commercial business or excluding contribution, we're still projecting growth for this quarter. For the full year range, $218 million to $228 million of ACV, we do expect potentially some contribution ACV in there. That's a component of it in the full year number.

But you don't want to break that out or quantify that?

Correct.

Okay. Okay. All right. Fair enough. And then in terms of Ajax, just how should we think about that flowing through? How should we think about that flowing through the P&L in terms of use of proceeds, anything like that? Is that in your guide for the year? I don't believe it is just timing and pacing of that.

Yes, you should answer that, but let me just say just to remind that our equity stake is around 6%. I just wanted to throw that in there. But Richie, please go ahead and answer the -- yes.

Yes, the Ajax sale was not contemplated in our guidance framework. Obviously, it's a private company sale that we couldn't have included, but its impact to our financials will mostly be to cash. Our cash position at the end of the quarter was $406 million. As Ramy just noted, we own about a 6% equity stake in Ajax.

And so when the upfront portion is received by Ajax, we will receive 6% of that approximately. So the impact to us will be cash. The upfront amount was not disclosed in the Ajax Lilly announcement. But as we receive the cash, we'll be able to reflect it in the balance sheet. And then on the upfront, there's also milestones kind of near-term and downstream milestone opportunities in which we would continue to have that 6% participation.

Okay. But I guess my question is, does that change in terms of how you think about investment priorities in the second half or just the fact that the balance sheet is going to be a little bit stronger? Any early thoughts on that or just going to wait and see for now?

I would say more of the latter. I think our path to profitability between growth in software and drug discovery as well as expense management over the 3-year window, that was all based on our cash position at the time. This is just upside to that. And we will -- once we receive the cash, kind of revisit if anything changes, but I would expect our 3-year outlook at the time to be unchanged.

Your next question comes from the line of Michael Yee from UBS Securities.

Great. We have 2 questions. First, maybe for Ramy. Just thinking about your overall P&L, you've got some very attractive 70% gross margins. But overall, as an entity, you're EBITDA negative and running operating losses. Given the general shift to reduce focus on moving things to later preclinical or clinical and looking to partner things, how would we expect the overall operating expense structure to potentially change?

In other words, what percent of your R&D do you estimate is going towards those types of programs? And if I back that out, could think about a more appropriate run rate of where you think your R&D could be? That's question number one. Appreciate, I think you have guided to sort of being EBITDA profitable in '28. So that's helpful. I wanted to know what percent of R&D is related to drugs.

And the second question relates as a follow-up. I estimate given I cover Lilly that Ajax could be like $1 billion upfront. So is the 6%, I think you said is not in your current cash guidance, so we should take 6% of whatever estimate is and apply the upside to the cash? And does that also -- is that booked in the income statement and flows through the income statement?

Absolutely. Richie, do you want to cover the second? Yes, and then I'll...

Exactly. So we can't comment on the size of the upfront, but the 6% equity stake we have is not in our cash guidance. I would expect it to run through our P&L as a nonoperating gain and not -- yes, as a nonoperating gain.

Okay. With regard to the question about R&D and drug discovery. So I think we've been very clear about this, that the drug discovery part of our business, which has been in existence for a long time since the -- even a little bit before, but around the founding of Nimbus over 15 years ago, has been an incredibly important part of our business and is highly synergistic with our software business.

We've shown, I think, very clearly that the success, the extraordinary success of these drug discovery partnerships, Nimbus, Morphic, Relay, Structure, Ajax have been -- have had such a huge impact on validating our platform, and they've also had a huge impact on helping us understand what it is that we should be working on, how we should be advancing the platform to have sort of the maximum impact on projects. So that will continue to the extent that there is still a huge amount of work to be done in advancing the field.

We're obviously incredibly excited about what we -- the accomplishments that we've made, and it's really been transformative. We've transformed the way molecules are discovered. That was our mission. I think we've been accomplishing that. But you can see through this initiative like the predictive tox initiative and many other initiatives like that, there's more work to be done, and we can continue to improve the way molecules are discovered, both in material science and life science. So again, that's a long way of saying that these businesses are highly synergistic, and it will continue to be an incredibly important part of our overall business model. Karen, I don't know if you want to add anything to that.

Yes. I mean I think as we've shared in the past, the vast majority of our portfolio, the combined portfolio of collaborations with our co-founded companies, with biotechs and with large pharma are an important part of the business, as Ramy just described, both from a scientific point of view, but also, as we saw this quarter, generating revenue.

And I would say that the vast majority of our activities actually in the R&D space are actually those collaborations. It's a small portion of the overall effort that is allocated to wholly-owned research. And as you heard previously on our prior calls, we will not be taking programs into the clinic. But we are also obviously partnering programs early, as you saw with the Novartis deal, partnering a program that hadn't even reached lead up yet. So our investment in R&D is partly obviously on the science side, as I'll say again, but it's also to create value. As you heard, we have 15 programs now with royalties on sales and revenue coming from these programs. As you heard across the whole portfolio, close to $700 million generated from our collaborative activities and the R&D drug discovery efforts.

Thanks, Karen.

We have guidance for 2028. That's helpful [ and positive for EBIT ].

[Operator Instructions] Your next question comes from the line of Evan Seigerman from BMO Capital Markets.

This is Conor on for Evan. We just had a follow-up on how we should think about the rollout of Bunsen and maybe kind of the phasing over the next couple of years. Of course, you have the upcoming early access launch this summer. We're just trying to think about maybe which types of accounts you'll be sharing access with in kind of the early summer launch? And then maybe as we think longer term and thinking about kind of understanding the throughput-based licensing, we're wondering kind of the functional rollout of Bunsen, will this be kind of a premium add-on or come included as a part of your standard software offering?

Yes. We're still working out all of the details of that as we typically do with early access versions of our technology, we work with our close partners, and we will do that the same thing here where we can work together to work out the sort of mechanics of integrating it into their workflows, but also checking on the science. Everybody listening to this call and all of us have had experiences that are mixed with LLMs.

Sometimes they're extraordinary and sometimes they do some pretty crazy things. So there's a lot of work that has to be done to make sure that we optimize and maximize the former and minimize the latter. And that requires working with close partners, of which, again, we have a large number. As far as the future, our expectation, of course, is that this will be ubiquitous and this technology will be available to all of our customers. Exactly how we price it is still to be worked out. That has a lot to do with this feedback that we get as we roll out this early access version. So yes, I think that's as much as we can say unless Pat has anything more to add.

No, that covered it perfectly.

Yes, yes, great.

Your next question comes from the line of Matt Hewitt from Craig-Hallum.

Maybe first up, as you -- given that Q4 is such a big renewal period for you, and you spoke to it earlier that you're starting to see some of those earlier conversions. Is it your hope and intention that you can get through some of that or maybe half of that before you get to Q4 just to kind of ease the burden or the rush that you would see at year-end? Or how should we be thinking about maybe the conversion over the course of the next couple of quarters before you get to Q4?

Richie, do you want to -- or -- yes.

Let me start. Thanks for the question. So I think the examples that we gave were more anecdotal and not the base case, but they were large contracts, and they -- we had a dedicated effort, I think, to try to convert those in advance. More broadly, though, the more -- the natural time for us to address the transition is on the contract renewal date. So I still would expect Q4 to be our largest quarter of the year for ACV.

Having said that, I think you'll see where there's opportunities, we will pull them forward ahead of the renewal date. Sometimes that relates to a new product, sometimes that relates to a new offering. So I think on the margin, you may see we'll do what we can to kind of pull forward and drive ahead of Q4, but I still expect Q4 to be our largest quarter of the year.

Yes.

Got it. And then maybe separately, with the strategic shift where you're not going to be taking internally discovered molecules into the clinic besides the ones that you've already got there, will you provide an update on how that is progressing? I mean, will you give us a, hey, we've discovered or we've got 17 molecules that are -- that we're working on right now and maybe 3 quarters later, now we're up to 20. Like how we monitor, how we know the progress that you're having on that internal molecule discovery side?

Yes. Karen?

Yes. I mean I think we have in the past, kept our pre-LO pipeline relatively quiet for a number of reasons. Obviously, you want to be progressing the program before you start announcing the identity of the program or the progress. What we have been announcing, obviously, is the deals that we've been doing. And so I will say we don't plan to kind of expand and expand and expand the size of this portfolio without actually transacting some of these programs as they move through the discovery space.

Again, as you saw us do with Novartis, we felt that those programs were well positioned to partner with that particular company because of their expertise and the synergy with those programs. And so you'll see us do more of that. I don't think you should be expecting an ever-growing early-stage portfolio, but updates as we identify partners for them.

Yes.

I am showing no further questions at this time. That concludes today's call. You may now disconnect.

Welcome, everyone, to our annual health care forum. We're kicking off things here with Schrodinger. Happy to have both Ramy Farid, the CEO; and Richie Jain, the CFO.

So welcome both of you, and thanks for joining us today on our virtual fireside chat.

I guess, Ramy and Richie, maybe walk through for the investors that are new to the story, because a lot has even changed in the last 6 to 12 months about the Schrodinger platform and how you've made some changes.

Of course. Yes, so I'll start us off. At the highest level, what our goal is, our mission is to develop a computational platform that allows researchers both in life science companies and in material science companies to design better molecules more rapidly, more efficiently. And that requires developing a platform that can replace experiment because the traditional way of doing drug discovery is by trial and error.

You make a molecule in the lab, you assay it, check its properties. And if it doesn't have the properties you're looking for, which, of course, will always be the case when you start off a project, you start to optimize it. So you try to make a change to the molecule. And obviously, that's very time consuming and prone to huge failure rates, as we all know.

So the whole goal of computationally driven drug discovery materials design is to do all of that on a computer and do it accurately, in other words, replicate the experiment and do it on a massive scale so that you can test huge numbers of molecules and find that perfect molecule that somehow magically balances all the properties that are required to be a drug or a particular material.

So what we have actually successfully done, and we would argue, and I think with a lot of evidence to back it up, that we are the first and only company so far that has developed a platform that can replicate experiment reliably on novel molecules, completely novel molecules, new chemical entities on a scale that is large enough to actually be able to find those very special molecules. And here's the key. It's validated.

We have for the last 15 years been actually using the platform ourselves to advance a number of drug discovery programs with companies that we either have co-founded or with pharma companies on our own behalf, and the success rate and the track record is extraordinary. We've got 15, 16 programs in the clinic. All the biotech companies that we have co-founded had really highly successful exits. Pharma collaborations are going well. Our own programs are progressing and going very well.

And on top of all that, we've been licensing that software to the whole entire industry. Every pharma company is using our platform, some at different scales, some at very large scale, some at a little bit lower scale. But everybody is using it, we'll get to that in a second. And essentially, our customer retention rate is 100%. What does that mean? That's another validation. I mean you don't keep renewing an annual license contract if the platform isn't having a profound impact on the projects.

Now here's the thing. The most exciting sort of technology that we've developed is relatively new actually. And so we're still in the early days of sort of scaling up and achieving the true TAM of this business, which is far larger than where we are right now at around $200 million per year. We have a handful, let's say, roughly of pharma companies that are using the technology at scale. That's very exciting. But there are other companies that are sort of still ramping up, and we see that as a tremendous opportunity for growth in the coming years for every pharma to be using the software at scale.

And then the last thing I'll say is we're very excited about -- we have a big investment in the platform. We're a science company, an innovation company. We're leading the field. And this year, it turns out that we have a few very exciting new products that we've released, probably one of the most exciting is predictive tox. It's one of the biggest challenges in drug discovery, is predicting toxicity associated with binding to off-targets, and we've released that this year.

And so we continue to make scientific breakthroughs, lead the field with new products. We expect that will continue to also lead to growth in the coming years from adoption of these new technologies as we continue to reduce the time it takes to get to a development candidate in drug discovery or a material and increase the probability of success with much higher quality molecules.

Great. And I'll just add a couple of the recent changes that we've made reflecting the strategy that Ramy just outlined. We have made some changes to kind of simplify and clarify the business structure. We had been executing a few programs in the clinic on our own. We are seeking now to partner those programs.

Ramy referenced 16 programs in the clinic that are advancing. Those are advancing in the hands of our partners, where we have downstream milestones and royalties on those programs. We put out a 3-year goal of achieving adjusted EBITDA profitability, which is achieved by growing both the software and drug discovery businesses and maintaining expense discipline.

And then we also announced a change to focus and emphasize hosted contracts in the software business as opposed to on-premise contracts. We think that will also take about 3 years to transition over. Today, it's about 25% hosted. We expect to get to 75% hosted. Given the way revenue recognition works, this is a very common transition for companies to make towards hosted and SaaS solutions. But in the near term, it will have the impact of reducing revenue, especially in 2026.

All the while the business is still growing. So we've changed and emphasized ACV this year as a business operating metric to track the business growth while the revenue catches up to ACV over the course of the transition.

Great. And maybe as a follow-up there for both of you. Why pivot towards this strategy now? Maybe I'll just broadly leave it there.

Yes. Maybe I'll start. You're talking about the transition, of course, from on-prem to hosted. Is that right?

Exactly. And moving away from the internal pipeline, away from the clinic.

Both things.

Both strategy. Why now? And why do this?

Yes, it's an excellent question. What we recognized is that the reason why we believe that we have developed this platform and why it's working as well as it is, why it's as validated as it is, is the result of this unique synergy between our software business, licensing the software to thousands of users, and using the software ourselves in collaboration with other companies or on our own behalf in the discovery phase.

What the result of that is, you can imagine how straightforward this is actually, that by using the software ourselves, we're learning what works, what doesn't work and we can improve it in real time. And of course, the validation is quite nice. All this validation that comes from creating development candidates in these high-quality molecules is very helpful in convincing a whole industry to change the way they do drug discovery. That's what we've done.

Drug discovery used to be done by trial and error, now we're using computation. And so that's what we wanted to focus on, those synergies. And that doesn't require running clinical programs. So synergies are sort of lost. Now you start getting into other factors, other things at play other than just designing molecules, which is where our core competency is, where our competitive advantage is.

And I'll tell you something else, too. I mean, to be very honest, when we started the clinical programs, since then the world has changed dramatically, what is occurring in China and the way they're able to do clinical trials much more cheaply and more quickly, the cost in the U.S., the Project Optimus, oncology itself. A lot of things have changed that have resulted in the cost associated with running clinical trials being quite a bit higher than we had expected.

So again, focusing our capital on where it makes sense, on these synergies, on the software business, on advancing the platform and on the discovery portion of our therapeutics group is a big reason why we made this change. I'll hand it over to Richie to elaborate on that. And also, it would be good to talk about the hosted, I think, transition too. That's important.

Yes, I'll cover the hosted piece, which is we have been moving towards hosted contracts gradually over the past few years and, through that process, have surmounted a number of key hurdles. We have transitioned some of our largest customers from on-premise deployments to hosted as well as doing initial deployments hosted.

But with our largest customers, we've passed all the tests from vendor audits, supplier audits, quality requirements. We've passed the bar with our most difficult customers. And customers are increasingly moving towards cloud-based solutions, especially in the Western world. So it satisfies their objectives. It also -- as we were looking through the business, a number of our deals were moving towards hosted solutions over the last couple of years at the customer's direction.

And so we were kind of reaching a tipping point where it made sense to move all the way over. From a customer-facing point of view, from a support point of view, we can support better, we can deploy faster. We can get the customer up and running in a shorter amount of time from when we get the order to when we can deploy.

From an investor point of view, obviously, our profile over the last few years has been lumpy just given the on-prem accounting recognition rules. And so we think this will provide a better picture for investors to measure our business and have a more smoother, predictable profile.

And finally, from a renewal perspective and an ongoing support perspective, we are just better positioned to understand the value customers are getting from our services, from our software and ensure that they're deploying it properly across their organizations, that every site, every location is using it uniformly such that when we approach a renewal, we are better positioned to understand their needs and our needs and capitalize on that opportunity.

Great. That sums it up perfectly. Okay. Moving on to the end markets here. How is the funding environment? What are you seeing budget allocations looking like? Maybe we'll talk about that broadly and then we'll go dive into maybe across the different tiers, the customer tiers that you guys are organized around.

Yes. I think like has been widely reported, we're obviously, like these other reports, optimistic about what this year looks like, especially compared to last year. So where you can see that we have guided to 10% to 15% ACV growth relative to lower growth last year, which was the result of the budget pressures both in pharma, which is sort of scary, but again, I think that's getting better, and obviously, in biotech. So we're confident with our ability to achieve that sort of growth, following a very difficult year, obviously, for the whole industry.

And just to add to that, Scott, our growth outlook for this year reflects not just one budget category. We have released number of new products this year. The [ leads ] are releasing a number of new products this year that will touch additional budgets. So that's a part of our growth story for this year.

Yes. Let's dive into that maybe more. So I think a big part of your next growth algorithm is unlocking more budgets that you haven't had exposure to in your life science end markets and your customers. Predictive tox is one of them. Let's talk about that strategy and where you think you can take that over the next several years.

Yes. So there are a number of things that are pretty exciting about that. The obvious thing, of course, is tapping into new budgets. It clearly increases the TAM for the business. But here's one of the things that we're most excited about with regard to predictive tox. Because of the nature of current computational methods, so the current methods require huge amounts of training because they're machine learning based solely. What does that mean? That means that they get done later in projects, way later.

And so what does that mean? That means that essentially, so you're doing a discovery project, working on a molecule, you're working on it for a few years, you've spent tens of millions, $20 million, $30 million, $40 million, 3, 4, 5 years. And then you'd start testing it first maybe using these machine learning models, which now start to work kind of because you've generated a huge amount of data, which is, of course, what's required when you're using machine learning-based methods. And if it lights up and is starting to show toxicity, that's it. Project is done.

It's very difficult to -- what are you going to do about it? I mean you have to go back to the drawing board and start redesigning the molecule even though you've kind of started to hone in all the properties. And here's what happens. You start trying to improve the toxicity profile and, of course, you start messing up everything else. It starts not being potent. It starts not being soluble. It's not permeable, whatever. It's a multi-parameter optimization problem.

The nature of what we've built is, first of all, it's highly accurate. But remember, I said at the beginning, it can be used on completely novel molecules because it's physics-based. It's not machine learning based solely. So what that means is that you can use it early in projects, way earlier. And of course, that has a huge impact on its TAM.

So not only are we tapping into new budgets, but we're creating a new sector in some sense, a new budget that is those same people that are running the toxicity, but moving it way up in the process, which requires, of course, way more usage.

And here's the other thing. With regard to these other solutions, they just tell you, yes, no. You're toxic or not. They don't tell you why. You can't do anything about it. The methods we've developed not only work on novel molecules, you don't need to train because they're physics-based, but you get a picture literally of the molecule, the structure of the molecule bound to the target that's causing the toxicity. And that means you can start to dial it out and, again, use it early on in the process as part of the multi-parameter optimization workflow.

Two follow-ups there. One, are these new products that you're developing in response to customers' needs, are coming directly from them? And secondly, can you build these all yourselves? Or is there a way that you need to have some bolt-on acquisitions to advance this growth initiative of developing more solutions to expand the budget?

Yes, it's a great question. It ties back to what I said earlier. Every company almost in every field struggles to innovate through asking customers what they want. There's the famous quote from Ford. He said, if he had asked people what they wanted back before cars existed, they would have just said, faster horses. And you've heard quoted like that from so many innovative companies.

In order to really understand what it is that's going to change things fundamentally and really innovate, you have to have a deep understanding of the problem. And in our field, where are you going to get that? By doing it ourselves. And that's why we have a therapeutics group and why we built it. So the impetus for developing this technology came from our own projects and our own collaborations.

We kept finding that we were running into this toxicity problem. Of course, it's off-target problem. In latent projects, you're hitting hERG and you discover that kind of late. Now hERG is an example where people are testing that a little bit earlier, but that's an example. And there's so many targets like that. So then what we did is we went and we had meetings with senior people at these companies and interacted with them and said, hey, what do you think about this idea? And of course, got really great feedback.

But the original idea has to come from within the company that's actually innovating to really make groundbreaking sort of scientific advances.

And then on the second part of the question...

To address the second part, yes, Scott, I think we have positioned ourselves from a balance sheet point of view to have capital. We have a capital position to fund the business for the next few years to get to the point of profitability. As it relates to M&A, within predictive tox, I think what we're developing is truly unique and has the potential to transform the workflow.

But just taking one step back and looking at where we sit in the entire workflow, if there are capabilities that are complementary to us, that are near where we sit in the workflow, we will, of course, consider M&A. I don't think we're going to do clinical trial optimization. That's very far downstream from where we are. But if we can find additional capabilities close to us that are complementary with the platform and are complementary with our customers, we'll take a look at that.

Great. Maybe let's talk about AI. On your recent earnings call, you talked about embedding agentic AI on your platform. Maybe just talk about the benefits of this and how many applications we can see with agentic AI as we head throughout this year and beyond.

Of course. Yes, it's very much on top of a lot of people's minds. So let's first make sure we understand really what agentic AI means, and then we'll tell you why it is something that we think is very important and what we're doing there.

So at the moment, you can imagine this being the case, especially when a technology is just sort of coming online and, again, a lot of companies and a lot of different spaces that are innovators run into this issue, which is there aren't a lot of experts that know how to use it and truly use it, use it correctly at scale. Of course, the scientists at Schrodinger can. And we put a huge effort into making the software easier to use, building workflows, writing lots of online courses and so on to train the next generation of computational chemists.

But another solution to that problem is agentifying technology, automating it, making it so that -- not replacing humans. And I think a lot of people understand this. You're not going to replace humans, but you're going to make them more efficient and augment their capabilities so they're not doing sort of the menial sort of things and allowing them to scale, make one human significantly more efficient, being able to support more programs and take advantage of this extraordinary technology.

So that's the goal and that's what we're working on. Now we should be clear. This is not easy. The agentification of many technologies is taking longer than people thought because it turns out humans are pretty good at driving cars, for example. And look how much longer that's taking. And that, believe me, is way easier than designing a drug, way easier, way less of a complex problem. But still, nevertheless, all of these are complex problems.

So if we can start to encode the knowledge that humans have, the most expert humans into the technology and make humans more efficient, obviously, that will have a really huge impact on the whole field and, of course, on our business as well because, of course, it increases the demand for the technology in a really serious way.

So we're very pleased to be working with a number of the sort of large companies that are building these LLMs. Anthropic is one of them, as we mentioned in our earnings call. Great discussions with them. We think this is something that requires a partnership like that, where we supply the expertise. But of course, the sort of foundation that these companies have built is quite difficult to replicate, obviously, fully internally.

Just to add two points, Scott. We've set up the business to capture this additional demand on the software. So what I mean by that is we have throughput-based licensing for the majority of our products. So as there are additional workflows being called by our user base or an expanded user base, we capture that all by selling on licenses and tokens as opposed to seats.

And then the other piece around agentification is that we have had historically a relatively small user base that have the experience and the capabilities to run our tools at scale. But with agentification, we expect that we can, over time, increase that user base by converting over chemists trained on traditional methods over to computational-based methods.

I think those are two really important points. One, you're not a seat-based. You're utilization-based, throughput-based. And two, that these agentic will be able to -- part of, I think, the barriers to entry was not having people be able to use the software in the most effective way, and having these agents should certainly improve that. So those are really important points.

Yes.

I guess let's walk down the financial model a little bit more just to clear any misunderstanding. So 10% to 15% ACV will translate into future revenue streams next year. This year is the sort of transition year. We're expecting profitability by '28.

Let me walk through that, right? So currently, in the legacy model, we had a lot of multiyear contracts that renewed in the fourth quarter that were not hosted. And we're moving towards a more stable quarterly distribution of revenue streams as we head out over the next several years. And then margins should take a stair step function lift next year and beyond as we get towards profitability.

This is a question for Richie. Maybe walk us through what the implications are for your guidance in case there's any misunderstanding about the financials of this process of getting to a more stable quarterly revenue project stream.

Yes, thanks. So at the end of 2025, ACV and revenue were in lockstep. Software revenue was $200 million, software ACV, $198 million. And to reemphasize, ACV and revenue will always equal each other over the course of time. So if we sign a $1 million contract ACV, the revenue will equal $1 million over the duration of that contract.

So in this year where we are in earnest starting the transition, and what I mean by that is the majority of our contracts are 1 year or less, so at the renewal, over the course of 2026, we expect to convert the majority of these contracts over from on-prem to hosted to the end goal of 75%. And why not 100%? There are always going to be some customers where conversion is just not an option. It's based on the geography, based on what the end market is.

But for the most part, Western world pharma biotech customers, we expect we will be able to convert. Actually, by the time we had our Q1 call, we had already converted over one of our pharma customers from on-prem to hosted. Over the course of the past few weeks, actually, we've tackled one of our multiple year on-prem deals and converted that over to hosted actually before the renewal date. So we're tackling these throughout the year.

But given the majority of the business is booked in Q4 just given the budgetary cycles of pharma companies and biotech companies, in that quarter when you switch from on-prem to hosted, you're going to go from on-prem, almost 80%, 90% revenue recognition in the quarter of the booking, to ratable. Let's just pretend a deal was booked November 15. You're going to be picking up 1.5 months of revenue recognition this year and creating a large deferred revenue balance that will be recognized in 2027.

So that is what has driven our focus on ACV for the year in a year in which we expect revenue to decline because of the phenomenon I just walked through. The key point for investors to really focus on is that everything I just said is cash flow neutral. So our cash flow from operations that you would see at the end of 2026 will not change from all of this accelerated transition to hosted. In that line item, it will be exactly the same irrespective of how we've approached the year.

And as you think about that over the course of the 3 years, we maintained the 10% to 15% growth trajectory for the 3-year period and expect to get to 75% hosted revenue. The reason we think those are important benchmarks are that revenue will start to converge with ACV. You'll start to see the two track each other. But as we continue to grow the business, we do think that ACV will be a leading indicator of revenue even after the transition period, going out into the longer-term future.

Great. And I guess my last question. We can open up to the audience. If there's any questions, please feel free to put this in the chat box here. We talked about new product releases to tap into different areas of budgets for pharma. I guess my question is, is there a set plan of how many new releases we can expect from you guys?

You did predictive toxicology and that went through beta for several years. And will that be the process, where you announce that you put into beta to test it with your customers to see? because obviously, putting it in beta actually helps you to get live feedback and make this the most monetizable product that you can and successful product that you can.

But how should we think about that in terms of the framework? Because it seems like it is a growing part of your growth algorithm, and it makes sense because you're tapping into all these new budgets that you had never had access to before. But how should we expect the cadence of new product releases?

Yes. We did something with predictive tox that we haven't done in the past. We've had many, many new product releases over the years and new ones that have tapped new budgets. But we haven't talked about the beta. We did it this time because, first of all, there was a rather large grant associated with funding this project, which is kind of unusual. That had an impact on our gross margin. So we were sort of talking about that.

But the FDA kept talking about the importance of computational methods for tox prediction and their attempt to reduce or maybe eventually eliminate animal testing, I don't think that's realistic, but certainly reduce it significantly. So we felt compelled to talk about it in advance. We don't normally do that. So it might look like something new was happening. It wasn't.

This is the normal cadence, multiple new product releases or major enhancements to existing products every year. We have 4 releases every year. We have a large R&D effort in this area. So there are new products coming out all the time. We won't always announce publicly the beta release. But we will certainly announce the -- and we may not even publicly announce actually on that. I guess, of course, to customers, obviously they're hearing about the new products. But this is a pretty normal cadence actually.

And it will continue into the future. There's a lot more to do. We're making fantastic progress. But as long as there's any failure in a drug discovery project or any project takes more than, I mean, even a few months, you should be able to get to a development candidate much more rapidly than we're doing now. There's still lots of new science to be done, lots more breakthroughs. And we'll continue to lead the field in those, in advancing the science.

And I guess maybe to end it here, we're coming up on the 35-minute mark here. Maybe for both Ramy and Richie, we've heard a lot about advancements in AI and pharma. This is a big application for pharma. What are your conversations like over the last 3 to 6 months with large pharma on this topic? Are there more inbounds than you've had ever before trying to understand your product offering? A lot of people will argue this is either a cannibalization or a catalyst for partnership. Maybe talk about what you're hearing from customers directly.

Yes. We see this as a tremendous tailwind, sort of the demand for AI is being used. That word is being used to really mean computation. So yes, the excitement around AI has over quite a number of years now dramatically increased the interest from traditionalists. Medicinal chemists are generally the ones running research groups saying, something is going on here. We need to be using computers.

AI is just used as a shorthand quick way of referring to computation. They understand what everybody, I think, needs to understand. AI is powered by training sets. It has no utility without the training set. That's what AI is. You have to train. And I think they understand very well because they've been testing this for many, many years, that experimental data alone is not sufficient to train these AI models. You have to generate simulated data, just like in self-driving cars, just like in chip design, weather prediction, every field. And those are simpler fields than what we're in. You need to generate simulated data.

They understand that they need our platform to generate the massive amounts of data that are required to actually power AI. So yes, everything is, I think, heading in the right direction and we're pretty excited about the future. And it's great that there's so much attention being paid to computation finally.

Yes. Well, that's great. I think this is a perfect place to end it. Well, thank you so much, Ramy and Richie, for doing this fireside chat with me.

Thank you. It's really great.

If the audience has any follow-ups or whoever wants to be in touch, please reach out to us. Thank you very much.

Appreciate it. Thanks, Scott.

Thanks, Scott.

Welcome, everyone, to back to our afternoon session of the 2026 Leerink Partners Global Healthcare Conference. I am -- for the audience, I'm Mani Foroohar, Senior Analyst at Jack Medicines. I'm hosting for this session, the team from Schrödinger. Ramy, Richie, how are we doing?

Great. Great. Great to be here. Thank you for having us.

Awesome. Welcome to my adaptive hometown.

Yes.

A little nicer weather-wise, perhaps than New York City.

Although apparently, it's 70 degrees there today. So we could have had this conference in just in New York.

We just had to pull you guys down. The delightfulness on the side. Let's talk about the evolution towards AC. I'm going to dive right in. Sure. dive right in. And I'm assuming people who know the general contour of the business. Let's dive into the transition to ACV. To what extent that is a change operationally? And to what extent that is a change in how existing contractual relationships are accounted for or recorded. And I think there's a little confusion amongst investors on, well, how are we able to change this? How much of just accounting for economic activity that would have happened anyways?

Yes. I'm sure we can clear it up. Richie, do you want to add...

Yes. No change operationally whatsoever. This is how we run the business. ACV actually gives a closer metric to how we run it on a day-to-day basis. The change here is really about moving to hosted contracts. We've been gradually going there over the last few years. This is just an acceleration of that movement based on what our customers are asking for, what our investors are asking for to give a better picture into the revenue visibility.

And it actually helps us support and deploy to our customers in a more accelerated way. So this is -- it's just a transition within our existing framework. It does have the impact of declining revenue this year. And just to spend a minute on that because it is kind of an unexpected thing. ACV and revenue at the end of 2025, both about $200 million. We are expecting our ACV to grow 10% to 15% a year. That is the operational metric for how we drive the business. Because we're shifting to hosted contracts this year, a hosted contract is recognized ratably over the life of the contract, an on-prem deal is recognized mostly in the quarter it's booked.

And because of most of our deals are in Q4, just given our customer budgetary cycles, the deals that we booked in Q4 by switching them to hosted, we will have limited recognition of revenue in that quarter, but it will even out over the course of a 12-month period. So because of that -- those 2 features, 2026, we expect revenue to decline and ACV to grow.

Over the course of a 3-year time period, we think that will start to even out as we transition over most of the book to hosted.

So on a channel, some of the investors that are a little less familiar with these dynamics. They would say, for a rapidly growing business, you booking revenue upfront is most appealing because you have lots and lots of new ads. Does this move towards a model where you recognize revenue more evenly over the course of the life of a contract, would that not suggest that perhaps this is a mechanism for the company to cover up declining contract adds? Are they all just plotting against us? Are you -- is this what you're doing, Ramy? Are you plotting against your investors?

Yes, of course, not. I haven't heard that, to be honest with you. I've heard that.

I

Tell me.

I see. Okay. So no, let's be very clear. Obviously, that is not the case. It seems to me, Richie, correct me if I'm wrong, but by actually giving guidance and tracking the business with ACV, that doesn't hide anything. That's exactly a reflection of the true growth of the business. If ACV grows, customers are adopting the software on a larger scale, new customers are joining. That's the only way you can grow ACV. You cannot hide anything. So it's actually kind of the opposite. Does that clear it up?

That has cleared up.

There's nothing further I can add there.

If ACV is not growing, the business is not growing.

And if ACV is growing, the business is actually growing. There's no trickery. I hate using that word at all in the sense, but I felt it seemed like we were forced to use it. So -- but the opportunity...

I agree.

Yes. Cool.

So diving into another dynamic that I think investors find challenging. is thinking about how to model and predict some of these larger, chunkier, almost biotech partnership like...

Fund flows...

Which are a little different than the base sort of software business. How should investors track the health of that market, the demand for those partnerships and sort of the pricing power, i.e., the terms you can demand in those deals?

Yes. Well, I think the first thing to say is we have an unbelievable track record with these collaborations. And really, I think it's underappreciated. Probably that's our fault. We haven't talked enough about it. But we've done a very large number of collaborations starting from co-founding companies like Nimbus and Morphic and StruXure and Ajax and the success of those companies has been unmatched. You don't see this kind of success in biotech companies. We formed quite a number of pharma collaborations. Those have been successful with what did we report $650 million in cash from equity stakes, from milestones from upfronts in the last 5 years. That shows a real track record, which, by the way, is not an accident. I mean that doesn't happen by chance. That's happening because the platform is resulting in better molecules being designed and that's having better outcomes in the clinic.

We have at the moment, 16 programs in the clinic for which we have royalties on sales. So this is a real business. This is not an NF1, it's not an NF2. It's not -- I mean, you can't catch -- it's 16 actually, right? What I just said as far as just that, and it's many, many more collaborations. So -- but the other thing I wanted to point out that's really important, I think, missed a little bit is that, obviously, all the things I just said, it stands on its own. But the other thing that's really important to understand is the impact that, that's having on the software business. We've changed the way drug discovery is done because of the success of companies like Nimbus and Morphic. Drug discovery is now done differently in pharma because of -- because we've demonstrated for the first time that you can actually use computation to replace experiment.

You don't have to make molecules by trial and error. You can actually do a lot of drug discovery on a computer. That's new. That's because of our platform and the success of the program. So all that success and validation has resulted in a change in the industry and how computation is deployed. And obviously, we're the main benefactors of that right now. So I think that's what's important to keep in mind the synergies between the businesses. They're not separated. Does that -- I answer in any way what you were asking?

It gave you some perspective. Okay. I think one of the other questions that people have is the defense market. There is broad concern around most businesses that sell software of any sort. I'm speaking very broadly. Well, what's going to get disrupted by AI, what's going to get displaced by AI? Are 4 kids somewhere is going to vibe code, their own Schrödinger. Talk about why that's not going to happen. How you think about parts of the business or parts of your counterparties that are susceptible to that kind of disruption or replacement and reasons why part are not.

Yes. Good. So there are 2 aspects of that. One is, can an AI model trained on experiment replace physics. That's one. And then the other is, can AI be used to actually generate the physics engine. okay? That's the one you asked about, but I think both of them. Now I think we've addressed very clearly the first thing. You need physics. You need ground truth to train AI.

So AI models are not going to replace physics. Great. Okay, we've put that one to rest. Now can a couple of kids with cloud code and a garage rewrite enterprise software of the level of sophistication that we've developed over the last several decades. The answer is absolutely not, and it's totally ridiculous. I mean it really is completely out of the realm of possibility. I don't think anybody who's understands these technologies or either the technology, the AI technologies or the technologies that underlie -- the science that underlies what we've done, think that, that's a serious question. Sorry, I don't mean to insult you. I mean because you're channeling the question.

This is nowhere near the worst insult.

Yes. I didn't mean it. But no, no, look, it's a legitimate question. Obviously, people are asking it all over. And by the way, hundreds of billions of dollars of value are disappearing because of a belief that this is -- so it's a real thing. You have to ask the question. I'm just answering it. The answer is unequivocally absolutely not possible. The amount of proprietary knowledge that goes into these technologies, the amount of deep understanding of novel things that the AI doesn't know anything about makes it impossible for AI to replace the kind of software we're doing.

Now that's not true, [indiscernible] I think you were hinting at this. I mean, let's talk about maybe legal software. So I hope that doesn't insult anybody or anybody's family member. But sure, there are fields where it's possible to learn, right, that there's enough in the public domain in books, right? I mean we see what -- how powerful LLMs are, where you can start to imagine those companies might be a little bit worried. And not for a little while, it's still a number of years away, but those are going to be the first to get replaced. But what we're doing, we are so far away from that being a threat that this most definitely does not keep us up at night.

Now here's the thing. We use this technology very extensively internally. It's helping to make our developers more efficient. So we understand very deeply what its capabilities are. So when we tell you this is not replacing what we're doing, I hope people believe us.

So you talked about the value of proprietary knowledge. I'm going to pivot over to proprietary data, sort of data as an asset and how we can think about the value either in resulting cash flows or improvement of the platform, how we want to sell, however we should measure it of the collective pool of data you have from the various experiments and the calculations you've done at scale for both your own programs and for your customers over the course of the life of the company.

Okay. I understand why that's being asked because everything always feels like it's about data, but let me put it into context. I don't think the amount of data, and I'm going to give you an analogy in a second, but the amount of data that's being generated even computationally is not going to power AI, and I'll explain why.

The idea behind your question is that maybe if we accumulate enough data. Whether it's experimental or computational, we can start to build a foundational model that can explain all of chemistry, every property that needs to be predicted against any protein or any confirmation of protein. And that turns out not to be the case. You will always, always need to generate new data using physics for every new problem that you encounter. That is. You have a particular target. You're going after a particular pocket. In other words, a particular confirmation of that protein. And you have a particular family of molecules, it's called the chemotype. You need to generate hundreds of thousands of data points to train an AI model to be able to predict one of the properties for that system. That model gets thrown away. And then you need to regenerate another hundreds of thousands of data points for the next problem, new target, new pocket, new chemotype and that will always be the case. That will always be the case. There is no -- this idea of a foundational model for chemistry, for design of molecules doesn't exist. So it's not about the data. It's about the ability to generate the data, and that's the physics engine that's unique to Schrödinger.

While we're here, let's talk a little bit about -- not just talk about the engine. We talked about some of the debates around AI risk. I want to talk about how to think about monetizing your existing position. So how should we think about the roll-on of predictive toxicology and how that additional feature service, whatever phrase you want to use, how that affects the value to the customer and how you monetize that on a per contract basis?

Yes. So predictive tox or the ability to predict tox, toxicity is one of the grand challenge problems in drug discovery. It's probably the major source of failure, maybe aside from biology risk, but it's a big problem. And it's a problem that occurs very late in discovery. So right, in other words, you're designing a molecule and everything is all great and then you go and do that one test either in vitro or in vivo and you discover toxicity and that kills the program. That's the end of it. That could be 5 years, $30 million down the tubes.

So it's a big, big problem. And we've come up with a way of addressing a major source of toxicity, which is binding to off targets. There is a huge amount of interest in this, obviously, given what I just said. We have now results from beta testing that is better than we expected. It's -- like I said earlier, it's now as good as doing experiment. The way this is done experimentally is you take a molecule and you actually put it into in vitro and you test against a whole panel of off targets. It takes a long time to do it. It's expensive and you don't do it very often. And now there's a computational way of doing that.

So now to answer your question, sorry, a little background, but customers have to pay extra for this. It's a new module. So it doesn't just get thrown in. It's also tapping into new budgets. So if our budgets before -- if our software was being sort of purchased by research groups, this is now by the toxicology groups, which are a little bit further down and I think generally have bigger budgets actually. The total spend actually on predicting toxicity of molecules is hundreds of millions of dollars done experimentally.

So it's a huge opportunity, really, really big opportunity. So we're excited to be launching it this year. We're excited about the beta feedback. We think this is going to be a major contributor to growth over the many years as we continue to develop it. By the way, it's not done. We have -- in our panel of off targets, we have maybe 60 or so off targets. There's probably on the order of many hundreds of off targets you have to worry about. That's the experimental panels are that big.

So we will continue to expand and continue to improve the product and continue to generate more growth from it over the coming years.

I'll just -- I'll add maybe 2 comments there, which is this helps us expand our addressable market. As Ramy said, reaching additional budgets, but it's expanding our capabilities within an organization. And second is that our entire business today predominantly is monetization of on-target discovery. This is off-target discovery. And so it has the ability to expand our applicability significantly.

Yes. So let's talk about...

How to think about that -- the scale of that -- I'm not asking for guidance. But how to think about the scale of that contribution as a new module and on what time horizon that shows up in ACV inflection, potential acceleration of growth rate of revenue, how we want to think about that?

It is built, by the way, into our guidance. We expect to generate revenue from it ACV, I guess, I should be saying. We expect to generate ACV. We have to get used to using ACV as our new metric for a little while. This year, given the positive feedback, extremely positive feedback we're getting from beta customers. But as is always the case with a new product, this has happened to us before. We have a lot of experience releasing new products. But we have a history of creating new markets. That's what we're doing. We're creating new markets.

So this is a new thing. So it takes time to get customers familiar with it with the idea of doing something new, testing it, they have to test it. They have to -- they don't just buy it on our promise. They have to test it. So we expect there to be a ramp-up and for it to grow over the years, but we do expect to see some portion of the growth that we've guided to this year will come from new products, and one of those is the predictive Tx module.

We've talked a little bit about the new module. We've talked about some of the nuances of interpretations of this pivot to ACV. Let's talk about end market growth. To what extent are you -- is your growth levered to new company creation? I know the majority of revenue is not from new company creation or new account creation. But how should we think about if we see a reacceleration of IPO market, VC market, et cetera, how does that flow through to you guys in terms of end-user demand? And what is that for you marginally?

It's -- I'd say we're not banking on that for this year. We're encouraged by the signs in the biotech markets, equity markets. There's a lag between those -- what's observable in fundraising and M&A and the translation of that to acquiring software and doing discovery. So we're not relying on that for this year, but over the course of the 3-year growth forecast that we've given, we are expecting biotech and the rest of life science markets to return to historical levels.

You're also looking at other growth markets outside of drug discovery...

Materials, et cetera.

Where are we in terms of the maturity of the platform for those applications? And how should we think about their contribution to the growth profile?

Yes, that's a really great question. We started that division because it turns out physics is physics, and atoms are atoms. And a lot of the problems in material science, it turned out we could leverage the physics-based these fundamental first principles methods that we had developed for life sciences. Polymers are polymers. So protein is a polymer, but the polymer that coats the airplane wing is also a polymer made up of the same types of organic elements, and we can start to try and understand the properties of those polymers using the same technologies. We have sense develop new technologies that are very specific to material science.

Batteries is a good example. The electrochemistry and the -- that's occurring at the interface of electrolyte and electrode in a battery, it turns out there's no exactly biological relevant sort of system. So we have been developing new technologies, in particular, technology around battery chemistry, which requires something called machine learn force fields. Essentially, these are a type of force field that is somewhere between the classical force fields that we use for modeling in drug discovery and quantum mechanics. It's got the accuracy of quantum mechanics and the throughput of classical force fields, new thing, very exciting work. We, for the first time, have been able to simulate that chemistry that's occurring at the electrode, electrolyte interface, which is -- has the potential to allow us to design better batteries for which there's clearly fantastic demand.

So I'd say we did a pretty good job getting the business up to a certain level, leveraging existing technologies. But now we're in this innovation stage where we're saying there's some news. And I think you know that was funded by a rather generous grant from the Gates Foundation. We've invested that. That's paid off, but it's just coming online. We're just starting to publish some of the work. So we're really optimistic about the future as we did -- like we did in life sciences. We've been doing so much innovation. We've changed the field. These free energy methods we've developed has been transformative. We think the same thing can happen in materials, but it's earlier days.

I you say earlier days, is the right way to think about the materials side of the business as a growth driver that primarily lives on the other side of the 3-year guidance that you've given? Or should we think of it as a meaningful contributor within the context of this 3-year period?

It's both, actually. I think we will see -- we're expecting that growth in material science business will contribute to the overall growth in this 3-year period. But given what I just said, it's true potential and maybe the real inflection is probably just a little outside that.

Okay. That being the case, what should we think about as the sort of trigger point of turn for that inflection? Is it a technological development? Is it adoption amongst a group of executives who are not used to using these tools? Like what is the event we should look at, okay, this is a sign that we should start modeling more growth from there?

Yes, that's a fantastic question. In pharma and biotech, starting maybe 10 years ago, something like that, was a transition of real acceptance and adoption, I think, driven by us of using computation to design molecules. The material science world is behind. It's behind that. It's being used, but you can tell by the number of computational chemists in material science companies is way lower than it is in pharma.

Pharma and biotech, and it really started much longer ago actually, has embraced the idea of using computation. It wasn't working very well, but they embraced it. That's something that hasn't happened yet. That transition has to occur. Now of course, we have to facilitate that. Why would they do that? If there's no technology that's actually useful, why in the world should they invest in computation. But I think that's what has to happen. And it's a chicken and egg problem, right? I mean they're not going to invest in that until they see the technology. But then if they don't start using the technology, they're never going to see the impact. So it's a little iterative process. But -- so I'm not sure I'm answering your question directly, but that's how we will see it.

You might not be able to see it. But when we see material science companies embracing computation and the way the pharma industry did a decade ago, that will be the sign that all of a sudden, that field is about to be transformed by computer-aided design just like our drug discovery field has been.

In pharma, a big part of that was people training to become scientists, being early adopters in academia, exposed to academic labs as part of their PhDs, exposing their students to it. That was a fairly long lead time.

It's one of those things that it takes forever, takes forever, then it happens all once when it happens. Makes it hard to model the material.

I know. Well, but we are putting that effort in. We have a significant education effort. We put so much work into getting academics using the technology. We actually developed shroding or developed online courses to give to professors to teach computation to students. So yes, it's going to take a generation, not a whole 4, 5 years, right, as students work their way through. But that investment works so well on the life science side. We're convinced it will work on the material science side, too.

Okay. And I think that captures a lot of where we are in that side of the business. Looking forward, past the other side of the 3-year plan, again, nice new guidance. You've given a real clarity on a path to profitability.

That's right. By '28.

Fast forward, we're sitting here in '28. You landed some -- you landed there, maybe a little higher, you're profitable.

Sure. Looking forward, how do you think about use of capital in the very long term for Schrödinger? Once you're profitable and growing, obviously, operating margins in this kind of business are pretty attractive. Like what is the right use of incremental capital?

So one of the things that is exciting, at least for me and I think a lot of people in the company is that we're never done when it comes to the platform and innovation. I mean drug discovery is still incredibly hard. There are a huge number of failures. It costs a ridiculous amount of money. It taking 4 years, 5 years to get to a development candidate, that's not okay.

It should be taking a year. And the failure rate should be 0 when you get to a certain point. So we will always be I hope this is the goal is to always be the -- we have been. We've been the leader in this space. We've been defining what it means to innovate in this space. And I think we are many, many decades away from saying, "Oh, we're done, everything is all good now.

Now drug discovery is as good as it's going to get. And don't forget, you have the whole material science, which, by the way, isn't just one field, material sciences, a huge number of different fields, right, from aerospace to chip design to battery design and countless other things, other types of materials. So that's kind of an exciting thing to be a part of to be able to drive the field forward and keep making it so that we don't have to wait for 15 years for life-saving medicines or materials that change. So that's a big part of it.

Now I think the other part of it is one of the things that has been frustrating for us is we've played a key role in generating an unbelievable amount of value for companies that we've been involved in cofounding. But we've owned a very small part of those companies. And I think as we get to a point that you were just describing, there will be an opportunity for us to own more of it. And I think that will also be really great for Schrödinger, for shareholders. So that's another thing that we're looking forward to. That's a little hard right now, obviously, but do you agree?

Yes. Just to quickly expand on that is we spent some time on the Q4 call laying out our portfolio of milestones and royalties. But by that time period, I'd expect to see some of those contributing on a recurring basis at a near 100% margin. So...

Yes.

Awesome. On that note, we are already over time.

Yes, we're a little...

That's good.

We look forward to continue the conversation soon.

Thank you so much. Great discussion.

Thank you.

All right. I think we're going to get started here. Thanks, everybody, for joining us. Welcome back to the 46th Annual TD Cowen Healthcare Conference. It's my pleasure today to be joined on stage by the entire Schrodinger team today, minus Karen, who is certainly missed. But maybe we can kind of just go down, Ramy, on my right as the CEO of Schrodinger. And on the far right is Richie Jain, the CFO. And then replacing Karen today -- nobody can replace Karen.

So maybe I want to kind of keep this as interactive as possible here. I will be checking my phone for any questions that kind of come in at [email protected]. But maybe I do want to kind of kick us off with the conversation, Ramy, give you a minute, talk about more substantive update we got last week when it comes to kind of the shifting towards hosted services. ACV reporting relative to revenues. Ultimately, what that means and kind of what all of us in this room should keep in mind?

With regard to the transition to hosted, yes. So I think I'll hand it over to Richie in a second. But the most important thing to understand is -- well, there are a few things. One is, this is a transition that we started a number of years ago. And it's been pretty successful. We already have -- roughly 1/4 of our revenue is already hosted. And the way we deliver the software to the customer, or the experience, let's say, even that the customer has is exactly the same. The price is the same. The way actually even the contracts are done. They pay upfront, and then they have a license for a year. It's just an accounting difference where the revenue, if it's hosted, is recognized ratably over the term of the contract versus if it's on-prem, where it's recognized mostly in the quarter that it closed.

So we think this is actually good for customers. It's certainly a better experience for customers. If the software is hosted, we can support them better. We can see what they're doing, monitor their usage, and a lot of our customers are always bumping up against licenses. So that's something now we can see and we can talk to customers and say, "Hey, you're not utilizing this, or you're bumping up against the licenses. You might want to think about purchasing more licenses." So I hope that gives the highlights. But Richie, anything you want to add to that?

Just to add, we look at the business on a cash flow basis, and this change has 0 impact to cash flow. And so because of that, we felt like ACV as an operating metric gives the best sense to investors on how to measure us this year, given that the revenue will have a lot of noise introduced to it because of the accelerated change to hosting. So ACV is a metric we went out with, and it's the closest metric to how we actually run the business.

Okay. And maybe just because you all mentioned this, I think, a little bit on the call, just to kind of clarify, so the kind of revenue and margin impact into the rest of this year and kind of through into next year, how should we kind of just think about that transition too?

Yes. So again, from a cost of goods sold, operating expense point of view, there's no change to the dollars. We do expect revenue to decline this year given most of the business is booked later in the year. And as we transition that over to hosted, you just have less days, less weeks, less months to recognize the revenue for this year. But this will all -- whatever revenue is not recognized this year will be recognized in deferred revenue and will be revenue to be recognized next year. So because of the -- but just mathematically, because we expect revenue to reduce this year, gross margins, our adjusted EBITDA will be impacted just numerically for the same reason.

I think it's also important to point out, if it's okay, I just want to say one thing. This is industry standard. This is a transition that most software companies are undergoing. So this is well understood. The impact, that's temporary, it has on revenue is well understood. A number of companies have gone through this. And this is the way you need to use some kind of metric like ACV to track the growth. So we're not doing anything that...

And just to add on that, the more effective we are in converting this year, the lower the revenue will be, which is obviously a little counterintuitive, but that is the way the math works, and that's actually the way -- what you want. And so given that dynamic, we chose to focus the guidance this year on ACV, because that is, again, truly how we run the business, and it gives you the best long-term view on where the growth is. Given the revenue decline that we expect this year, that is a snapback, and we expect that all to be picked back up in the '27 reporting year. So it's a multiple year transition, but the first year will be the most off the path you'd expect to see.

Okay. Understood. All right. So I know a lot of this kind of comes against the backdrop of this transition and kind of the overall business strategy for the company, right? So maybe let's talk -- start at kind of high level, but then how this kind of feeds into the strategy overall, really kind of just the state of the computational platform today. I guess where are you kind of seeing all the changes and evolutions you've made in recent years? What is kind of drawing new customers to you? And what is kind of keeping customers coming back for more as you undergo this transition now?

Yes, that's a super exciting thing that's happening now. I don't know if people fully appreciate how extraordinary advances have been made in computational chemistry, in the ability to actually run a computation, run a calculation that displaces an experiment that you don't have to run the experiment. That's an amazing thing. So we're doing things now that weren't possible even just 5 years ago. So using the physics engine that we've developed, we can predict so accurately key properties of molecules that you don't have to run the experiment. That's an amazing thing to be able to say. We've been doing this for a long time -- we've been dreaming about this for a long time, and now it's possible.

Now the incredible thing about this is that we can run those calculations on a scale that is dramatically higher than anything you can possibly do experimentally. So you have the accuracy of experiment, but you have a scale, let me put some numbers to it. We can, in 1 day, generate as much data as it would take 10 years to generate that data if you did it experimentally. Now what does that mean? That means now you have this extraordinary amount of data way more than you can possibly produce just using experiment that you can use to train AI models.

So AI models are now allowing us to scale the physics to even larger space and allowing us to now explore massive amounts of chemical space. What does that mean? That means that we are now, for the first time, able to significantly accelerate the time it takes to get to a development candidate and improve the probability of actually getting there. And then maybe the most exciting thing is you're doing that now with much higher quality molecules. Because you've explored such a huge amount of chemical space with very high precision that the probability of success in the clinic is much higher.

Now those aren't just words. You're hearing a lot of these words from a lot of places, and it's hard to figure out the signal from the noise. But we've been applying this technology now for a number of years. And the result is quite a number of programs that have actually gone into real programs. This isn't just saying we've solved drug discovery with AI. We've produced 16 clinical assets, a number of them in late-stage clinical assets, for which we have royalties and milestones. And the newCos that we've cofounded have had really tremendous success, a rate of success that is definitely better than the industry average. So I hope you understand what the technology is doing, how we're able to combine this physics engine with the scale of AI and actually delivering valuable assets.

And now one of the most exciting things now that is happening is advances in agentic AI that's allowing us to scale this now in a really dramatic way. One of the limitations in applying the technology is experts to be able to use it. And we're pretty excited about advances in workflows, but also in agentic AI that will allow us to really scale this platform. I think that's what's bringing -- all of that is what's bringing customers.

Yes. And I think -- I mean, this touches actually on a few conclusions we had from -- last night, we had a panel with the R&D investments in healthcare AI. We had the heads of R&D from Novartis and Takeda and Relay Therapeutics as well. And Fiona from Novartis, she gave you guys a great shoutout.

Yes, that's great.

Yes. So I mean I think it gets at some of the disconnect between some of us on the outside who are not using a lot of these tools every day, and we just kind of see pharma pouring more and more money into what the rest of us are kind of collectively deeming AI, but ultimately what that impact means. I think fundamentally, some folks assume, well, if they're spending more internally on AI, that means that there's less dollars that they're willing to spend externally rather than what it seems to be that they're now identifying where the holes in their data are and who they need to kind of turn to help plug those holes and train the models.

Exactly. I think there's been a -- this is well understood by the experts. It might not be so well understood by investors and the general media, but it's very well understood that these pure AI models that are trained solely on experimental data are very limiting, because by definition, drug discovery is about finding new chemical matter, right, novel IP. And that means that predictions of those molecules will not work because they're not in the training set. So you need physics. You need first principles methods to build those training sets. And as I said before, you can do that now, thanks to advances not only in the physics-based methods that we've developed, but huge advances, thanks to NVIDIA in hardware and then, of course, the ability to scale all of this with AI. So...

Pretty exciting. I mean -- okay, so we talked kind of a little bit about the actual transition and reporting for the software business. But all things else considered, how should we think about maybe the next 12, 18 months? Any kind of important inflection points in the actual growth of the software business itself when you have the predictive tox offering is now in beta testing and remains widely...

Yes, it's released.

So as we kind of think about your assumptions for guidance within the context of ACV revenues, but realistically just actual growth of the business over, let's say, the next 18 months?

Yes. So first of all, predictive tox is a really big deal. This is one of the great challenges in drug discovery. It's very common for a drug discovery program to run for 5 years and you do all of this work and spend all this money to identify a development candidate and then you start going into tox studies and you discover that there's a tox problem, and that's the end of it. That's it. You don't know how to fix it. You have no idea where the toxicity. So it's a major source of failure. So this is a big deal. We have launched it. It's come out of beta, because the beta feedback was really incredibly positive. There's a huge amount of excitement, obviously, in solving this Grand Challenge problem in drug discovery. So we expect to see growth from predictive tox this year and into the future.

There's a lot more work to be done. There are, in principle, 20,000 proteins that you don't want to bind to, right, the proteins in the human genome. And there are some estimates that it may be a bigger number. We're at roughly 60, 70 off targets that we've enabled in this predictive tox panel. We're adding more as we go. So that's going to be a big area of research and of growth.

Now the other thing is what we were talking about before. Not every pharma company -- you met a few of them that are using the technology at scale to generate these training sets for AI, but not every pharma company is doing that yet. They're using it. All of them are using it, but they're not using it at full scale. So we're expecting that to change. We think there have been enough companies that have transitioned to the sort of large-scale use of these methods that it's sort of derisked. And now it's just a matter of more companies sort of adopting the technology at scale. So that's another major source of growth this year in the time frame that you said and beyond, just the actual continued scale-up of the usage.

The other area that -- a couple of areas we're excited about, too, is biologics. Our platform has largely been developed and validated in small molecules, but obviously, biologics are very important. We've been putting a lot of work into that, both on the informatics side, but also on the physics side, and there have been a number of advances there that we expect to be able to contribute to growth.

And then if I can just touch -- I know this is a health care conference, but I'll just say just very briefly, these physics-based methods can apply to other systems as well, because physics is physics. And so we're pretty excited about the work that we're doing in battery chemistry. Again, I won't spend a lot of time on this, but there are very interesting material science applications in pure material science, workflows like in design of batteries. But in pharma, formulations is a material science problem. And we have new products in that space as well, in particular, crystal structure prediction, which is an incredibly important part of formulation and drug discovery that we also expect to contribute to growth this year.

Pat, did you want to add? Is there anything else? Did I cover everything?

Yes, a lot of it. I think one place that we're investing in, too, is beyond just the physics simulation, but we've had the LiveDesign platform that Novartis talked about using to own data. It's really important to be the central platform of drug discovery that allows you to -- anyone who's building any AI models, you want them using it through your platform. Then you become that central hub, which we've successfully done for the strong majority of pharma in the small molecule space, but we have introduced now a large molecule offering. It's especially tempting for people in ADCs and peptides, which are a little hot these days. But because both small molecule and large molecule technologies may be necessary for these, we're uniquely fitted that we understand both of these very well. Most software companies don't. And we're very excited about our LiveDesign for Biologics application. We think that's a great growth opportunity.

Brendan, I'll just add, we're an R&D company. We invest in R&D for our customers who are doing high science R&D. And the investment is to expand our addressable markets. So within life sciences where we've existed predominantly, a lot of the new products we're introducing are immediately adjacent to our customers today, but touch new budgets, touch new capabilities. And as Ramy mentioned, in material science, there are endless end markets there. So we're really excited about the opportunity. We don't typically talk about new products before they're released. Predictive toxicology was an exception to that, just given the amount of industry attention on it and the FDA mandate around new approach methodologies. But some of the other products that we've rolled out on our call last Wednesday are the way we typically release, which is we develop the product and then we launch it to customers.

And I'm glad you bring up the NAMs, right, because it's kind of next natural question that comes out of this a lot. So maybe just help us understand like where this predictive tox offering is actually situated within the FDA initiative, right? And then maybe in that same breadth, when -- I guess I should first ask, have you gotten new customer inbounds or from existing customers specifically tied to some of those initiatives, and when we should realistically think of the impact of that to software growth?

So at the moment, of course, the computational methods that have been developed for predicting toxicity are wanting. They're not very good. And so there's a very heavy reliance on doing it experimentally, which is time-consuming and expensive. What does that mean? It means it gets done pretty late in the process, as I was alluding to earlier. And then it's just -- that's it. Program is dead. You just lost a huge number of years, and that's a real issue. The methods that we've developed, again, as I said really earlier, are highly accurate, really predictive. They predict whether a molecule will bind to one of these off-targets, so-called off targets that are associated with toxicity.

And what that means, of course, because it's a lot faster and a lot cheaper to do it computationally and experimentally, it can be moved up really upstream, really early in projects. So in other words, it becomes part of the multiparameter optimization of a molecule. You do it really early on and you make sure that by the time you get to the end, when you get to a development candidate, you've addressed not only affinity and solubility and permeability and so on, but you've also addressed selectivity and therefore, toxicity. So I think there are 2 applications. One is new. It's a completely new market, right? People using this early in discovery. And then, of course, it's still really valuable in the later stages when you're starting to think about what molecule to put in animal studies, and that's where it ties into the FDA.

Now the FDA is saying they want to eliminate animal testing. I think every time somebody hears that word, they think, come on, that's crazy. But it's okay. It's okay to think crazy, because the future isn't so far away. Right now, it isn't going to eliminate animal testing. But it's clear, and it has been reducing it, because, of course, if you have a molecule that's lighting up in this computational assay and saying it's going to be toxic, why would you put it into an animal? So it will certainly result in reducing animal testing. Maybe in the future, it will eliminate it. That seems really far-fetched that you would actually use humans to test the toxicity, but it's okay. You get the idea. It's going to significantly reduce it. So I think those are the 2 applications.

And one other thing I'd elucidate is that it's not just for animals, we can test both the animal and the human protein. So you might go through the entire development process just looking at the human protein and then it fails in animals and you're like what just happened. But since you can test both of those, you'll be able to uniquely identify ahead of time. If you see something in animals that's different than what you're seeing in humans, you might be able to know ahead of time and expect that or vice versa. It looks fine -- in the worst-case scenario, it looks fine in animals and it has a problem in humans. Knowing about that earlier, obviously, is incredibly valuable, because clinical trials are even more expensive than the animal studies. So that's extra knowledge that just largely doesn't exist right now. There are some experiments to try to get at it, but that level of information and be able to push that early should dramatically increase the success.

Got it. Okay. So when we kind of think about now continued evolution of the platform, and I promise you all continue to come back to this question over the months and quarters ahead. But now that you're all kind of transitioning really to a fully-fledged software entity with a few notable exceptions around the edges there, how do you kind of think about continued evolution of this, right? Obviously, we have predictive tox, but you mentioned biologics, you mentioned a few other modalities. Is it kind of order of operations to expand within what you've got to other modalities and then maybe to other parts of the drug development spectrum. Like where does that kind of strategy fall?

Yes. As Richie said, we're an R&D company. We have a significant investment in the platform. One of the areas that we're super excited about right now, and it's actually the -- what I'm about to describe is a technology that's actually enabling the predictive tox initiative. And that's, in general, just protein structure prediction. So there have been a lot of advances in computational methods and experimental methods for determining the structures of proteins. You heard about it, AlphaFold. I mean there's Nobel Prize, right? But what's not so well known is that the output of those initiatives, the experimental and the computational, is pretty low-resolution structures. They're not actually that useful out of the box.

We are developing methods for refining those structures to high resolution, which is actually what you need to make use of them. You got to get the details right. If you hear me say the word physics, right, physics-based method, you can imagine the input to a physics-based method is getting the positions of the atoms in the right place as a starting point. And so that's pretty important. So we're putting a huge effort into determining the structures of proteins and the molecules that they're bound to, to high resolution.

Now what does that do? At the moment, we really only know the structures of proteins, human proteins to high resolution of maybe 10%, 15% of the human proteome. Obviously, the ability to scale that up to 100% allows us to work on targets that we otherwise can't work on, the so-called hard-to-drug targets, targets that are implicated from the point of view of biology in important diseases, but we just don't know how to target them. Is it with a small molecule? Is it a peptide? Is it a degrader? If it's a small molecule, is it a macrocycle? Is it small? Is it big? Is it -- right? All that sort of thing.

So enabling us to actually explore all of biology through knowledge of the structure is huge, to really open up -- and again, that's the technology that's being used to enable us to be able to predict binding to off targets. But obviously, identifying or being able to design molecules for targets of interest from the point of view of solving diseases is obviously a really big -- is important and a big area of research.

Yes. And I just want to add on, too. We have the ability to see our most popular workflows that are used in our software. And by far, our most popular workflow is one that takes PDB structures and cleans them up, because your average PDB structure is so far away from being usable in drug discovery. And I think this is really important because most of these AI models are trained to try to reproduce the exact PDB structure, which our customers are telling us, through that utilization, are not good enough. So the best case scenario is they're reproducing at the same quality that is not good enough for use, and that's why we really invest in that.

And PDB structures, that's the structures in the public domain.

Yes. And I guess kind of tied to this now, before we get into the therapeutics pipeline just in the last couple of minutes, I did want to ask the partnership strategy. I know you all kind of announced a new partnership with TuneLab over at Lilly. And I think it's kind of more focused on this idea of kind of federated learning, right? But maybe help us understand how that approach and that specific partnership relative to like the Novartis, right, that we talked about before, how does this all now fit into Schrodinger's overall partnership strategy and where that kind of fits into the growth story for the software business overall?

Yes. We're super excited about TuneLab. TuneLab covers a huge gap that biotechs have. So for as long as companies have had LiveDesign, which is approaching 15 years now, which is kind of crazy, every big pharma has put in these machine learning trained tox models built on all of their data. They've gotten a little better at it, but really, it's just kind of around the margins. And one limiting thing is how much data they have.

But what we see happening is when people leave pharma and they've gotten used to doing drug discovery with these ML tox models that they've built, they go to found a biotech and there's nothing. There's no public because this is all built on their internal things. So what Lilly has done, it's awesome here, is they've figured out how to give every biotech out there access to those types of models. And obviously, Lilly is not just doing it for fun. The biotechs then put their data back in and Lilly's own models get better.

But it's super exciting for us because selling LiveDesign this entire time, the first question is, do you have any suggestion for how I get this model like I had back at Big Pharma X? And our answer has historically been no. Now I do want to address the elephant in the room, because we get a lot of questions, isn't this directly competitive with predictive tox. It's not. So the accuracy of these type of models is totally different. They're very useful. They're often on endpoints. They're much higher level than what we simulate, but the correlation between the endpoints is much lower. It is still useful, obviously. But when they're using our engines, they typically expect an accuracy that they can make confident decisions in. And these are more kind of like red light, green light hinting accuracy. Still very useful. I don't mean to denigrate it at all, but it's just a different tier.

I just want to add to your kind of long-term growth strategies and how we partner. What we see as a long-term driver is enabling our users to become power users. We have throughput-based licensing and pricing. So the more any individual user uses, we are able to capture that value. But as the workflows become more efficient, we can enable our users to be able to run more and also expand the amount of users who can run our technology. So Pat is actually working on a lot of the integration with LLMs and other agentic AI processes that will be able to expand our user base over the years.

Okay. Great. So I think now in the last few minutes here, I did want to touch a little bit on kind of the status of the therapeutics pipeline, right, both internally and partner here. So maybe just quickly give us a sense of when we could get updates from 3515 and 1505, that's the Wee1/Myt1 and then MALT1 inhibitor, respectively. And ultimately, kind of what the status is of both assets, whether that's external licensing and partnership discussions, where that all stands now?

Sure. I'll address that. So our intention is to finish the dose escalation studies on both 1505 and 3515. We presented data on 1505 last year. 3515, we should be presenting data in the second quarter of this year. But importantly, we've announced that we see the best way to advance these assets are with partners in the mid- and late-stage development. So that's where the focus is. We'll give updates as we have them.

More broadly, as we think about therapeutics, we continue to be really excited about the collaborations portfolio, working hand-in-hand with our pharma partners and not only generating IP and delivering development candidates, but enabling broader adoption of our software within those customers and also generating downstream milestones and royalties that we're accruing at this point. And we're excited about the targets and the indications and the royalty rates that we have there. We gave some additional disclosure in our results last week to kind of give a sense for what that opportunity is. But 5 of these programs are in $5 billion-plus markets where we have royalties ranging from high single-digit to low double-digit ranges.

Okay. So it's fair to assume now kind of in perpetuity moving forward, at least for the foreseeable future, that any new therapeutics, new drugs that could come out of the Schrodinger platform would largely be relegated to existing partnerships that you have with pharma biotech externally, right?

That's right.

All right. So I guess just in the last minute here now, I want to kind of pull everything home a little bit. So we've talked about the transition with ACV. We talked about therapeutics pipeline. We talked about the partnership strategy and evolution of the platform. So with all of that said now, as you kind of look ahead, not just into the latter half of this year, but really into kind of this next era for Schrodinger, where is kind of the biggest disconnect when you talk to folks who are trying to understand where you all fit in, what the real growth drivers here are and ultimately, where they are kind of trying to value the platform?

Yes. I think the biggest disconnect, and you can't blame people for this, is there's a lot of noise out there. I mentioned it earlier, right? There are an uncountable number of companies labeling themselves as AI companies that have completely changed drug discovery, have solved the problem. And they're publishing blogs and publishing white papers and doing comparisons and getting a lot of attention actually. And it must be really overwhelming for -- I mean, how are use supposed to do unless you're an expert in all things, physics, chemistry, biology and computer science, to figure out the signal from the noise.

And what I would invite people to do is just look at the track record. That's important, right? What have these companies actually produced? What have we produced? And I think if you do that, there's a very clear distinction between companies that are just saying that they're doing things and running retrospective analysis, right, running calculations on things that are already in the literature and saying, look, it matches up. As everybody knows who's developing machine learning and AI, that's really easy to do, because those things are in the training set. So that doesn't count. And if you haven't produced development candidates and clinical assets and you don't have 100% customer retention from customers, I'm not sure you should be out there doing all of that.

So sorry, I know that sounds a little bit critical, but you can imagine the frustration from a company that has been doing that for a long time, has had a track record of delivering over and over again high-quality clinical assets that are progressing through the clinic, for which there are very meaningful milestones and royalties, by the way, on quite a number of them. And the success of the companies that we've co-founded is striking. I mean it's noticeable. That's not normal for that sort of success rate and then the size of these exits and so on is really meaningful. So I hope I keep saying it and having an opportunity in venues like this to highlight it. I would encourage people to go look at it, look at the pipeline, look at the success. I hope you will see a difference. Don't take our word for it that we're just saying we have a physics engine that's accurate. I mean, look at the results. I think that's really important.

And I think the other thing is, don't be -- it's very dangerous when there's a new technology, it's so easy for it to get overhyped. You have this tendency to sort of extrapolate in the future and say, well, I don't understand this stuff, but boy, it sure looks like it's going to be able to do something that is magical and I don't really understand. Usually, that isn't the case, right? It's a technology like everything else. It has a domain of applicability. It works in certain circumstances. It doesn't work in others. Treat it like a technology, not like magic and something that you should be really afraid of. I don't know if I'm saying anything useful, but it's on our mind. I hope it's useful.

Yes, looking at the actual use case for a lot of the tech resistance. All right. Well thank you guys for joining. It's always a pleasure to see you. Thank you, everybody for listening. We got more to come.

Thank you very much.

Thank you standing by. Welcome to Schrodinger's conference call to review fourth quarter and full year 2025 financial results. My name is Rob, and I'll be your operator for today's call. [Operator Instructions] After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions] Please be advised that this call is being recorded at the company's request.

Now I would like to introduce your host for today's conference, Ms. Jaren Madden, Chief Corporate Affairs Officer and Head of Investor Relations. Please go ahead.

Thank you, and good afternoon, everyone. Welcome to today's call during which we will provide an update on the company and review our fourth quarter and full year 2025 financial results. Earlier today, we issued a press release summarizing our financial results and progress across the company, which is available on our website at schrodinger.com.

During today's call, management will make statements that are forward-looking and made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995, including without limitation, statements related to our outlook for the full year 2026, our medium-term objectives, our plans to accelerate the growth of our software business and advance our therapeutics portfolio, the timing of readouts from our clinical trials, the clinical potential and properties of our collaborators' compounds, the use of our cash resources, as well as our future expenses.

These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially due to a number of important factors, including the considerations described in the Risk Factors section and elsewhere in the filings we make with the SEC, including our Form 10-K for the year ended December 31, 2025. These forward-looking statements represent our views only as of today, and we caution you that, except as required by law, we may not update them in the future whether as a result of new information, future events, or otherwise.

Also included in today's call are certain non-GAAP financial measures. These non-GAAP financial measures are not prepared in accordance with generally accepted accounting principles and should be considered only in addition to, and not a substitute for, or superior to GAAP measures. Please refer to the tables at the end of our press release, which is available on our website, for reconciliations of these non-GAAP measures to the most directly comparable GAAP measures.

This afternoon, Ramy Farid, our CEO, will review our recent progress and 2026 outlook. Then Richie Jain, Chief Financial Officer, will review our financial results and discuss our 2026 progress and 2028 objectives. Then Karen Akinsanya, President, Head of Therapeutics R&D and Chief Strategy Officer, Partnerships, will review our therapeutics portfolio. We'll then open the call for Q&A.

With that, I will turn the call over to Ramy.

Thanks, Jaren, and thank you everyone for joining us today. Schrodinger is the leader in advancing and deploying computational methods for molecular discovery. We have developed a highly differentiated computational platform that integrates physics, AI and a scalable data infrastructure that is dramatically accelerating molecular discovery across life sciences and material science R&D globally.

Our software business generated approximately $200 million in annual contract value in 2025. We also have a collaborative and internal therapeutics portfolio that leverages our computational platform at scale. We have an extensive and unmatched track record of delivering high-quality development candidates that has generated a high-value portfolio of future milestones and royalties on sales.

Before highlighting our 2025 results and 2026 outlook, I would like to take a few moments to describe our vision for the future of molecular discovery. Our goal is to virtually generate all relevant molecules, computationally assay their key properties, and select the optimal molecule. We have made significant progress toward this ambitious vision, already achieving substantial reductions in the time and cost required to discover differentiated drugs and materials. This progress is powered by our unique computational engine that is an integration of the most advanced ground-truth physics and cutting-edge AI. Our platform's value is clearly validated by our high customer engagement, clinical success in our therapeutics portfolio, and the success in our research collaborations and the biotech companies we have co-founded.

The AI landscape is expanding rapidly, but the fundamental challenge remains the same. AI models require massive amounts of highly accurate training data. Because chemical space is nearly infinite, experimental data alone cannot provide the scale needed, and therefore ground-truth physics simulations are often the only way to overcome this data scarcity problem. This physics-first strategy is already a proven standard in complex fields like autonomous vehicles, weather prediction, chip design, and aerospace. By leveraging this proven physics-first approach, we are pioneering the next frontier in drug and materials discovery. Schrodinger is the only company with a physics engine accurate and validated enough to power the next generation of AI for drugs and materials.

We are uniquely positioned to lead the next era of molecular discovery by scaling our physics plus AI platform across a multi-billion-dollar growing market. Several industry tailwinds are accelerating demand for our technology, from deeper biology insights and a surge in available protein structures to the evolution of faster compute and agentic AI. At Schrodinger, we are leveraging these breakthroughs to provide the ground-truth calculations essential for training next-generation models. Additionally, as agentic AI drives higher utilization of high-compute calculations, our throughput-based licensing model ensures capture of that expanded consumption by our customers.

Our strong performance in 2025, highlighted by 23% total revenue growth and a strong cash position of $402 million, provides significant momentum as we enter 2026. In 2025, we expanded our platform capabilities for biologics and materials, released the beta version of our predictive toxicology solution, and advanced our portfolio of proprietary and collaborative programs. We were also pleased to see molecules we co-invented advancing within our partners' clinical pipelines. Karen will discuss this in more detail shortly.

Looking ahead, we are focused on achieving 10% to 15% software ACV growth, operating with expense discipline, and accelerating our transition to a primarily hosted model, which Richie will discuss shortly.

We expect to continue to drive increased adoption of our platform through product innovation, addressing both our well-established and newer markets in life sciences and material science. Within our therapeutics portfolio, we plan to complete the Phase I studies for SGR-1505 and SGR-3515 while advancing our collaborative programs.

Our software sales strategy is focused on scaling platform adoption and broadening our reach. Among our existing customers, we are focused on supporting them in leveraging computation at scale and have consistently observed customers increasing usage as they experience firsthand the outcomes of our predict-first approach. We are also targeting additional budgets within existing customers and unlocking opportunities in large markets such as biologics, toxicology, synthetic chemistry, drug formulations, consumer packaged goods, chemicals, energy capture and storage, and electronics.

At Schrodinger, we do not just follow AI trends. We are leading the way with our gold-standard platform, where physics-based simulations provide the ground truth required for AI to navigate the endless universe of molecules with precision. We are therefore uniquely positioned to leverage AI in a way that cannot be replicated or replaced by a global AI model. We have set the standard for molecular discovery, one that delivers faster design cycles, higher success rates, lower costs and fundamentally better molecules. We have significant opportunities in the year ahead and look forward to updating you on our progress in the coming months.

I will now turn the call over to Richie.

Thank you, Ramy, and good afternoon. Schrodinger had a strong 2025 delivering $256 million of revenue or 23% growth against a challenging backdrop of tight pharma budgets and challenging biotech capital markets. The software business generated $199.5 million of software revenue and $198.5 million of software ACV with strong growth from our commercial customers. The drug discovery business generated $56.4 million of revenue from our portfolio of collaborative programs. With over $400 million in cash, we have a strong balance sheet to invest in growth while targeting positive adjusted EBITDA by 2028.

Our full year results demonstrate balanced growth. Software revenue increased 11% and drug discovery revenue more than doubled compared to the prior year. Software growth was primarily due to an increase in hosted and maintenance revenues, and drug discovery growth reflects the continued successful execution across our portfolio of first-in-class and best-in-class discovery collaboration programs and the continued progression of these molecules. Software gross margin was 74% compared to 80% in 2024, reflecting higher costs associated with contribution revenue from grants in 2025 compared to 2024.

Total operating expenses were $310 million, a decrease of approximately 9% compared to 2024. This reflects rationalizations in R&D and G&A from our 2025 cost reduction initiatives, offset by a modest increase in sales and marketing to continue investing in long-term software growth.

Total other income was a gain of $65 million compared to $24 million last year due to mark-to-market changes in our equity investments and currency fluctuations. We'd like to congratulate our partners at Structure Therapeutics, a company we co-founded, for their December announcements related to GLP-1 and their Phase I amylin program.

Net loss for the year was $103 million versus a net loss of $187 million in 2024. The fully diluted share count was 73.4 million compared to 72.7 million in 2024.

I will now turn briefly to our fourth quarter 2025 results. While the ACV for Q4 2025 and Q4 2024 were similar, software revenue for Q4 2025 was $69.3 million, a decrease of 13% compared to fourth quarter 2024. This is partly a function of the upfront recognition of revenue from a large multi-year on-premise deal signed in Q4 2024 compared to fourth quarter of 2025 in which portions of several multi-year deals were deployed as hosted, deferring most of the revenue recognition into 2026 and future years. This had the impact of reducing our software revenue recognition in Q4 2025, but reflects the accelerating transition of customers to hosted. The other line items for the fourth quarter reflect the same trends as described previously for full year results with a gross margin of 81% and operating expense discipline.

Given our focus on driving software growth and the expected near-term volatility in reported revenue due to our accelerated transition to hosted, we are introducing a new set of software KPIs that better track our business objectives and enable the measurement of our progress. Total ACV increased to $198.5 million from $190.8 million in 2024, representing 4% overall growth.

We continue expanding our top 20 pharma relationships, and ACV for this cohort grew by 15%. Commercial ACV, which includes the rest of life sciences and material science, grew 7% to $177.4 million. We continue to focus on growing existing commercial relationships with $1 million as a key threshold that demonstrates adoption of our platform at scale.

Our average ACV in this cohort increased to $3.9 million from $3.3 million, or 16% growth. Within this cohort, 2 of our largest customers were acquired in 2025 by top 20 pharma customers. These acquisitions are a strong reflection of the impact of large-scale adoption of our platform. And while these acquisitions reduced our customer count by 2, their throughput and value were largely retained.

From a retention point of view, we are shifting to dollar-based metrics focused on our commercial customers. Net dollar retention, which measures growth less churn from existing customers but excludes the growth from new customers, fell to 100% after several years averaging over 110%, reflecting the incredibly difficult environment for pharma and biotech in 2025 that impacted our ability to meaningfully expand relationships last year. We continue to see strong renewal performance as demonstrated by 96% gross dollar retention, which only measures churn from existing customers, underscoring the essential nature of our platform.

In drug discovery, the successful expansion of our partnering activities since 2018 across 20 separate collaborators has increased the number of programs from 13 to 16 that are eligible for royalties on sales that mostly range from high-single-digits to low-double-digits. We believe there is significant embedded long-term value in the milestones and royalties associated with our portfolio that Karen will review later in the presentation.

Overall, we remain pleased with our performance in 2025 looking across the composition of our ACV. As discussed previously, we achieved 7% growth across commercial customers and 15% within top 20 pharma. The rest of life sciences, including our biotech customers and government academic, reflected a well-understood challenging funding environment for 2025 that we were able to withstand. Our materials science business continues to scale up, growing from $15 million to $17 million as we introduce new capabilities. Finally, we continue to make great progress on the predictive toxicology and battery chemistry modeling initiatives that are partially funded by the Gates Foundation.

Building upon several years of gradually increasing our hosted revenue mix and building out capabilities and processes to support our largest and most sophisticated customers, today we are announcing an accelerated transition to industry-standard hosted contracts that are increasingly preferred by our customers. Our business mix today is predominantly on-prem, resulting in lumpy revenue from mostly upfront recognition of contracts, in particular, multi-year contracts. Starting this year, we have begun prioritizing hosted deployments that support the continued trend toward cloud-based solutions and allows for faster deployment, enhanced renewals and licensing and support efficiencies. From a revenue recognition perspective, this will also result in more predictable revenue.

Over the last several years, we have transitioned several of our largest customers from on-prem to hosted deployments, supporting their audit and service level requirements and resulting in 23% of our software revenue as hosted for 2025. The goal is to transition approximately 75% of our software revenue to hosted by 2028, factoring in that for some regions and some customers, a transition to hosted is not likely based on our current expectations. Given the accelerated transition, I will highlight the key accounting considerations for revenue recognition while noting it will have no impact on total ACV or cash flows.

Hosted revenue is recognized ratably over the duration of the contract. So for deals booked later in the year or that have longer duration, this will result in reduced revenue recognition in the year the contract is booked with a corresponding increase in deferred revenue or backlog for future year revenue recognition.

As a rule of thumb, we expect each 1% increase to hosted revenue percentage to result in a $2 million to $3 million reduction in current year revenue. The acceleration of this transition began in January, but because the majority of ACV is booked in Q4 of each year and our largest customers are on multiple-year agreements, many of which do not renew in 2026, we expect to see a modest increase in hosted revenue percentage for 2026 and greater acceleration for hosted revenue percentage for 2027 and 2028.

We expect revenue to begin converging with ACV by 2028, but that ACV will generally be a leading indicator of revenue as the business continues to grow. Given the near-term reduction in expected revenue, we expect this will also compress gross margins and adjusted EBITDA without any impact to cost of goods sold or operating expenses. Reiterating that the accelerated transition to hosted has no impact to ACV or cash flows, we expect a more predictable financial profile as we target 75% hosted revenue by 2028.

This hypothetical illustration captures the differences in revenue recognition for theoretical zero-growth $1 million annual contracts that vary in renewal quarter and duration between on-prem contracts on top and hosted contracts on the bottom. As you can see in the yellow box, $1 million in ACV will ultimately result in $1 million in recognized revenue regardless of the deployment. However, on-premise deals result in significant upfront revenue recognition with only maintenance for the remaining quarters, while hosted deals result in ratable $250,000 per quarter. This contrast becomes even more extreme for longer duration contracts, and the chart also demonstrates the revenue recognition straddling fiscal years for deals booked in Q4.

Today, we are providing 2026 guidance as well as outlining our financial objectives that collectively result in a target of achieving positive adjusted EBITDA by the end of 2028. Given our accelerated transition to hosted revenue, we are providing ACV expectations rather than software revenue guidance for this year. While we will continue reporting software revenue, we believe ACV will provide important visibility into the performance of our business during a period where we expect revenue recognition to be highly variable.

For full year 2026, we expect ACV to be in the range of $218 million to $228 million or 10% to 15% growth. Our expectation for Q1 is ACV of $24 million to $28 million compared to $25 million from Q1 2025, which implies $197 million to $201 million on a trailing 4-quarter basis.

We anticipate drug discovery revenue between $55 million and $65 million for the year as we continue to advance our collaborative portfolio. As we have said previously, drug discovery revenue has quarterly variability due to the collaboration and milestone-driven nature of the business.

Our operating expenses are expected to be less than 2025 as we fully realize the annualized impact of expense reductions and efficiencies announced in 2025 and maintain overall expense discipline.

Looking over the next few years, we are targeting annual software ACV growth of 10% to 15%, substantially completing our transition to hosted contracts, and returning gross margin percentage to the high 70s.

In drug discovery, we anticipate approximately $50 million of revenue annually, again, noting potential variability each year due to the collaboration and milestone-driven nature of the business. Our organization is aligned around these near-term and longer-term objectives. And our strong balance sheet with over $400 million in cash supports the path to positive adjusted EBITDA by 2028.

We've taken deliberate actions to manage expenses, invest in the platform to fuel software growth, and prioritize discovery-focused therapeutics that drive our multi-year financial objectives of 10% to 15% software growth, $50 million of annual drug discovery revenue, an accelerated transition to hosted, maintaining expense discipline, and targeting positive adjusted EBITDA by 2028. We are really confident about our future and the opportunities ahead and look forward to updating you on our progress.

Now I would like to hand the call over to Karen.

Thank you, Richie. Our therapeutics activities comprise 4 key value generation opportunities. Equity stakes in companies we co-founded over the past decade and a half have led to M&A transactions and cash distributions from Nimbus, Relay, Morphic, and Petra. We maintain equity positions in Nimbus, Ajax and Structure Therapeutics.

Licensing of our proprietary discovery programs and collaborations with biotech and pharma companies generate upfront payments and milestones. As Richie noted, the $56 million of drug discovery revenue for 2025 reflects the progress of our collaborative programs. Potential future milestones of up to $5 billion and royalties on 16 programs, as well as future value creation from wholly-owned discovery programs represent longer-term opportunities.

Our diverse ecosystem of collaborations continues to expand. This includes multi-target collaborations with pharma and biotech companies that we co-founded and were later acquired. This is resounding evidence of the impact of our platform, and successful drug discovery and financial outcomes for the molecules we co-invented.

The impact of our predict-first approach to drug discovery, pursued by Schrodinger's design and discovery experts and our partners, has resulted in a growing portfolio of optimized compounds. There are more than 25 active programs across the combined portfolio. Multiple oncology drugs across the portfolio have received Fast Track and Orphan Drug Designations, and approximately $650 million in cash, upfronts and milestones have been generated to date.

In addition to best-in-class compounds like Takeda's Phase III Tyk2 compound, zasocitinib, we have successfully discovered oral versions of injectable antibodies and peptides. We refer to these as modality switches, and they represent a powerful application of Schrodinger's platform and expertise in structure-based drug design.

Modality switch programs represent large market opportunities, and are associated with lower clinical translation risk, given that the mechanisms have already been validated through to commercialization.

Oral drugs have a lower cost of goods, greater ease of access, and importantly, enable combinations of incretins or combinations of immunomodulatory mechanisms to enhance responses and achieve durability in patients with cardiometabolic and inflammatory diseases.

The portfolio of programs that are eligible for royalties continues to grow and advance, with 7 clinical programs from Phase I through Phase III. The programs span a diverse range of disease areas and many undisclosed and disclosed modality switch programs, including oral Entyvio, the small molecule alpha 4 beta 7 compound being developed by Lilly after the acquisition of Morphic, and first-in-class oral amylin being developed by Structure Therapeutics. We believe there is an increased probability of success for modality switch programs to achieve the royalties for these collaborative programs.

In 2025, we were very pleased with the release of the Phase 3 data for zasocitinib, a best-in-class Tyk2 inhibitor co-invented with Nimbus, prior to its sale to Takeda for $4 billion. Takeda reported that they expect to launch the product in 2027. We are eligible to receive up to approximately $100 million in additional future cash distributions from the payments made to Nimbus based on global sales milestones.

In addition to the progressing collaboration programs, we are finalizing the Phase I studies for SGR-1505 and SGR-3515. We expect to report initial Phase I data for our Wee1/Myt1 co-inhibitor at a medical meeting in the second quarter.

Finally, our inflammation and immunology programs, including our best-in-class brain-penetrant and peripheral NLRP3 inhibitors and several modality switch I&I programs, continue to demonstrate promising profiles.

I'll now turn the call back to Ramy.

Thank you, Karen. As you have heard, 2025 was a year of significant progress across our business. We delivered strong financial performance despite a challenging macro environment and took decisive steps to position the company for long-term success. We are confident the strategic pivot we initiated last year, combined with the 2028 targets we've shared today, puts us on a clear path to achieving our financial and operational goals.

As always, I want to thank our dedicated employees for their exceptional work and accomplishments. We are energized by the momentum heading into 2026 and look forward to sharing our progress with you throughout the year.

At this time, we are happy to take your questions.

[Operator Instructions] Your first question comes from the line of Mani Foroohar from Leerink Partners.

Guys, thanks for walking us through some of the impact of that transition, which is one that many software companies have to go through and does require a little remodeling work on our part.

I want to hop over to think about strategically how you think about potentially the ongoing process of partnering out assets in your own pipeline, continuing to rationalize across your own pipeline programs as necessary or appropriate, and how to think about what impact that could have on potentially pulling forward the 2028 profit metric?

Mani, so with respect to partnering, as you know, this is an ongoing and active component of our business. Over the last 5 years, we have essentially done collaborations and licensed programs from our wholly-owned portfolio on a fairly regular basis, so it remains a very important part of the business. And as you know, we're constantly in conversation with potential pharma partners and others. So that will remain something that we will be very active in. As you've seen from the portfolio, there are a number of assets that are available for us to partner, both now across oncology and immunology.

I'll leave it to Ramy and Richie to talk about the potential impact, but obviously, we're not guiding to any BD today. So Richie, I mean, do you have any...

Yes. Mani, just on the second part of the question, I think the goal for profitability on an adjusted EBITDA basis by 2028 is a function of growth across the software business of about 10% to 15% a year and drug discovery revenue of about $50 million a year, and then continued operating expense discipline over the 3-year time period. As we have further updates on the portfolio, we'll keep you updated on those goals as well.

That's helpful. And as a quick follow-up, when we think about the ACV guidance, which we're reaching out all the way to 2028 now on that growth rate, I recognize one of the things that could drive additional growth, whether on revenue or ACV, however one wants to think about it, is additional tools flowing into the products such as the predictive toxicology platform. Is there any value baked into that growth for monetizing the value of predictive toxicology, or is that entirely incremental? Should that become a meaningful driver, that would be incremental to the 10% to 15%?

Yes, thanks Mani for the question. We certainly expect to generate additional growth from the new products that we're releasing this year. As you mentioned, predictive tox is one of them, but there are a number of other new products that we mentioned in our prepared remarks. In particular predictive tox, we're very excited about it. The feedback so far from the beta has been very positive. In fact, it's actually outperformed our expectations. So we're definitely looking forward to that. I mean, the launch is underway, so we're excited about that and we're looking forward to realizing growth again from predictive tox as well as new products that we're releasing this year and that we will be releasing in the future.

Well, the right interpretation is that there is at least a little bit of value for incremental growth from predictive tox baked into that 10% to 15%, or any incremental value -- or any value would be incremental to 10% to 15%. Just to clarify in case I missed it.

Yes, Mani, it's Richie. As in the slides that we presented today which we will make available on our website, we listed out a number of new products that are launching. And our growth expectations over the 3-year time period include the impact of those new products.

Your next question comes from the line of Scott Schoenhaus from KeyBanc.

And yes, thanks for all that color as we make this transition in our models. I guess I want to start on the first quarter ACV versus the full year ACV on the software side, 10% -- a little bit above 10%. And I understand you're still going through the transition and that 4Q is still your big renewal season quarter. But maybe walk us through the dynamics of the first quarter ACV for this year relative to the full year, please.

Richie?

Thanks, Scott. So as you noted, Q1 does tend to be one of our smaller quarters following the Q4 season, which is customary for software companies and a reflection of our customers' budgeting cycles and our contract renewal dates. ACV, as we shift to that as our metric, it only reflects the value of the deals that we closed in the quarter, whereas revenue, where we have guided to in the past as a financial metric, reflects deals closed in the quarter but also revenue from prior quarters. So that's why we've guided to that range, thinking through those considerations.

Also, given it is Q1 and it's a smaller quarter, the achievement of it is much more sensitive to each individual contract. So we do expect to grow in each quarter, but that's some of the thinking that went into the range that we set for Q1. I would reiterate that over the course of the year, inclusive of Q4, which is where the majority of the business is booked, we are expecting that $218 million to $228 million of ACV or 10% to 15% growth.

That's helpful. So any upside utilization would drive revenues above that ACV number reported in the first quarter. How are your customers dealing with this transition? We've always talked about in the past that you're never forcing this model, that customers sometimes preferred this, but now it's a clear adoption of a hosted platform. Can you just give us how your customers are engaging with this or reacting to this strategy now?

Yes. Hosted cloud-based solutions are an industry standard and allow us to deploy faster and also support in an enhanced fashion. Customers are actually increasingly preferring hosted deployments, and we've had a track record over the last several years of being able to support our largest and most sophisticated customers with hosted deployments. From an investor point of view, 23% of software revenue today is hosted, which we've been gradually increasing over the past several years. Because the revenue is ratable versus upfront or on-prem contracts, this will also result in a smoother and more predictable revenue profile as we target 75% by 2028.

Your next question comes from the line of Michael Ryskin from Bank of America.

This is Alex on for Mike. My first question is on AI. So there's been the focus of AI playing a bigger and bigger role in R&D processes and Schrodinger fits neatly into that. Have you noticed any changes in your conversations with pharma customers in recent months regarding this? Are they open to engaging and leveraging your solutions to drive efficiencies and become more computational? So in other words, is Schrodinger an AI winner in the R&D space? Or is pharma shifting funds and focus away from these traditional methods and prioritizing new solutions pioneered by maybe Anthropic or OpenAI? And then I have a follow-up question.

Yes. Thanks for the question. Yes, we view AI and the sort of new revolution of agentic AI absolutely as a tailwind. It's very clear that, that is -- the adoption of AI and the scale-up that it results in is actually increasing the demand for our software. The fact that we license our software using a throughput-based licensing model obviously means that we benefit from that scale-up.

The other thing that we've said, and you've heard us talk about this for years, is that one of the barriers to adoption of our platform at scale is know-how and just availability of humans to run the software at scale, and obviously, agentic AI solutions address that. We've actually -- we're working with Anthropic directly to explore ways to integrate agentic AI with our computational solutions. And we're -- and this is now implied in your question, we're actually excited about the broader adoption of these types of methods, again, because it increases the demand for our technology, not the other way around.

Great. Thank you. That's helpful. And my second question is about -- you talked about 2 largest customers being acquired. Can these customers kind of disappear in terms of usage over time? Or could this be a beachhead in terms that maybe it'll lead to more major pharmas like, learning about Schrodinger and helping to grow the business with pharma acquirers?

Yes. We -- despite the fact that it reduces the number by 2 on a KPI slide, this is actually a great fact pattern for us. It's a recognition of the predict-first approach and using computation at scale in the molecules that they're able to develop and resulting in M&A traits. It's no surprise that the companies that acquire them also have the same approach. So it is -- actually, we view it to be, despite the interim loss of a customer, we were able to retain the throughput and the relationship there and view it as a positive long-term signal.

Your next question comes from the line of Brendan Smith from TD Cowen.

I wanted to ask just a little bit more actually about your go-to-market strategy for this predictive tox launch this year. I hear you on the 10% to 15% kind of blended growth, including existing and upcoming product launches. But just curious if you're thinking that predictive tox will largely be an add-on within existing customers or if it will honestly require new touch points at some of those accounts or even if reps will largely come from exposure to brand new customer accounts altogether? Just kind of wondering how you're thinking about that initial sales outreach and potential impact on SG&A through that lens.

Yes, it's a very good question. Actually, it's both. One of the use cases for predictive tox is using it very early in discovery projects and to the extent that that is the case, we expect existing customers to acquire the technology. Of course, it is an add-on, so that requires them to pay more for it. And by the way, that's also a throughput-based hosted web service, which is the way it's being delivered. So from that respect, we'll see growth that way. But it also will be used by researchers later in discovery and preclinical development by toxicology groups who we currently do not -- or we don't -- they're not currently among our customers. So it will result in growth from tapping into those new budgets. So it's really both.

And Brendan, I'll just add quickly to that. A number of the new products that we're launching fit the same profile, which is they're reaching new end customers, new touch points within existing customers. This obviously will have the impact of adding to our total addressable market. You had touched upon how we operationalize this in your question; we have made some investments in sales and marketing. You'll notice it in our 2025 results to account for this and be able to execute against getting these additional products into the hands of customers.

Great. And if I could just stick with a quick follow-on, just when you're talking about the transition to hosted contracts and flagging the impact on margins, if we maybe just zoom out a bit from just the software margins, how should we think about the concurrent impact of that transition against overall blended margins kind of versus the backdrop of winding down the internal pipeline and presumably some of the OpEx savings you could see from that?

Yes, there's -- I'll tackle that. There's kind of 2 elements of the question. I just want to reiterate that the transition to hosted has no impact on ACV or cash flows. So while we expect there to be some interim variability in revenue because of the accounting recognition, and that will have an impact on margins, in particular with gross margins and adjusted EBITDA. The actual cost of goods sold and the actual operating expenses are -- have not changed at all by this impact. So we did want to give a longer-term view of gross margins returning to the high 70s, which is where we've been prior to the last few years when we took on the predictive toxicology grant that temporarily lowered gross margins and also give you a view of the adjusted EBITDA opportunity for 2028.

When we're looking at it from that lens, it's not only the growth in the software business and the transition to hosted, but it is also reflecting some of the operating expense reductions and efficiencies across the whole business, including the software business, the therapeutics business, and the overall enterprise that gets you to that adjusted EBITDA profitability benchmark out in 3 years.

Your next question comes from the line of Matt Hewitt from Craig Hallum.

This is Toph on for Matt. So just getting an idea on your 2028 goal of adjusted EBITDA break-even. What assumptions do you have baked in there about a biotech rebound? And then, do you consider for capital allocation any buybacks?

I'll take the second one first. We always think about capital allocation. We're very pleased to have the balance sheet to support our growth opportunity and also support our pathway to adjusted EBITDA. We -- from a buyback point of view - I mean certainly, the stock price is not one that we think reflects our intrinsic value, but we see a long growth pathway ahead of us and would rather invest our cash into growth in the business, as opposed to buying back shares.

In terms of the 3-year outlook on growth, biotech has been a challenge obviously for the last few years. We are assuming over that 3-year window to see a recovery there to normalize levels and growth within that segment.

Your next question comes from the line of Michael Yee from UBS Financial.

This is Kyle Yang for Michael Yee from UBS. So you guided software ACV to $218 million to $228 million in 2026 or roughly 9% to 14% year-over-year growth. It would be helpful if you could remind us and the Street, how do you define ACV? And how does it differ from reported revenue? Help us understand how this ACV guidance could potentially translate to reported software revenue in 2026.

And so given the transition from on-prem to hosted, and that's -- we would assume that's not going to be fully completed in 2026, would you expect this reported revenue could come higher than software ACV guidance?

And finally, just help us understand the puts and takes that would ultimately determine whether your revenue could land towards the lower or upper end of the revenue guidance range?

Okay. There's a lot of questions in there that I'm going to try to take one at a time. So ACV, annual contract value, it is reflecting the value of a contract. If it's a 1-year deal and we book it in Q1, for example, the ACV will be fully reflected in Q1. If it is a multiple-year deal, we will reflect the annual bookings of that deal for each year of the deal. So that is the definition of ACV.

ACV and revenue are actually quite different. We will post our deck to the website, but if you look at Slide 19, we have a pretty detailed visual explanation of the difference. You can see from a revenue recognition point of view in on-prem deals, there is a significant acceleration in the quarter the deal is booked, and then very little revenue in the quarters that follow. Whereas in hosted revenue deals, it's ratable across the 4 quarters.

So you had asked about expectations for 2026. We do not expect that revenue will be greater than ACV this year, given that we are very busy at work trying to transition our contracts over to hosted. For all the deals that renew this year, and for all new customers, our goal is to move them over to hosted. Our goal over the 3-year time period is to get to 75%, not 100%, because there are customers in certain regions and certain sectors where they have not been as aggressive in embracing cloud-based solutions, and so we don't think that they will convert over to hosted.

But given that the majority of our ACV is booked in Q4, and we intend to transition those over to hosted, it will result in reduced revenue recognition for that quarter, but you will see an increase in deferred revenue and backlog to capture the amount of revenue that will be recognized in the following year. We actually have not guided to revenue for this year, so I don't know how to respond to that comment, but we are guiding to ACV for the year of $218 million to $228 million.

And just to correct some math, probably there's a rounding problem, that corresponds to 10% to 15% growth not 9% to 14%, I think as you stated in your question. I just want to add one more thing just because it's sort of a math thing. If we are successful in transitioning our customers to hosted on this path to getting to 75% transitioned by 2028, mathematically the revenue has to be lower in '26 than it was in '25. That's just a mathematical consequence and again that table reflects that. I hope that's clear.

Great. And to the last question, could you please help us understand what could be some key puts and takes that could determine whether your revenue is going to land toward the lower or upper end of the guidance?

Okay. So just to reiterate, we are not providing revenue guidance for this year. We are providing ACV guidance. In terms of our ACV outlook for the year, it is based on expanding relationships within our existing customers and embracing the AI workflows and the demand that is generated out of that for our platform. It is based on rolling out new products that address different workflows for our customers and different budgets. And it's based on expansion within our material science business.

As a rule of thumb, which may help address your question, we are at 23% hosted software as a percentage of software revenue. In any given year, as we increase that percentage, each 1% increase will roughly result in about a $2 million to $3 million reduction in revenue for that year. So as we've put out a 3-year view to getting to 75% hosted revenue percent, we will make progress towards that. I expect in 2026 the progress will be rather limited on that basis because of the amount of business that's booked in Q4. But that should help give you a rough guide on a way to measure our progress. But the -- what this is revealing is why we have focused the guidance this year on ACV, which is an operating metric, and hosted revenue percentage; we think those 2 metrics will help evaluate our performance this year.

Your next question comes from the line of Evan Seigerman from BMO Capital Markets.

This is Conor MacKay on for Evan. We found some of the color that you provided on Slide 16 as it relates to ACV composition pretty helpful. We're just wondering if maybe you can share more on how you're thinking about the evolution of customer split as you look towards your annual software ACV growth goal of 10% to 15% by 2028. Maybe how might a gradually improving biotech funding environment and the addition of some of your newer products impact your customer split?

Yes. I'll take a crack at that. I think you're sort of asking about ACV growth of each of those segments, and we're not guiding to that. So -- I mean, as Richie just said, we do expect to see some recovery in biotech. We're seeing tailwinds from a lot -- bigger adoption of agentic AI. We have new products that are being released this year that we expect to see revenue from that should impact all of those segments, both in life sciences and in material science. So hopefully that answers your question without getting into details of each individual segment. Richie, do you want to add anything to that?

No, that's perfect.

[Operator Instructions] Your next question comes from the line of Sean Laaman from Morgan Stanley.

This is [ Morgan ] on for Sean. I had a question related to the last one, but more so dialing into the $1 million and above customers. So we saw this year that the ACV on average went from $3.6 million (sic) [ $3.3 million ] to $3.9 million. What were some of the key actions or key measures that resulted in that increase and what can you do to continue to increase that average ACV exponentially at this point?

Yes. So within that customer cohort, we think that $1 million threshold we think is a reflection of a customer adopting our technology at scale and embracing a predict-first approach. We grew that segment -- we grew customers within that segment from an average relationship of $3.3 million to $3.9 million, or 16% growth. And this customer cohort, you can say, includes a lot of our top 20 pharma customers, as well as a handful of biotechs that are adopting the technology at scale.

And so within those 2 groups, top 20 pharma customers have been our longest-standing customers, that's where we are introducing new products and growing relationships, as well as increasing the adoption, closing some of the adoption gaps between our largest customers in top 20 pharma and our smallest customers in top 20 pharma.

Outside of top 20 pharma, there's a handful of customers that have embraced this predict-first approach. Fortunately or unfortunately, 2 of them got acquired last year, but there are many companies that understand the power of computation and are fully embracing it across their organizations to predict fully optimized molecules.

Another encouraging thing in that cohort is, actually, there's a fair amount of variance within the cohort of customers spending over $1 million. So that's an exciting opportunity that even within that group there's still significant potential for the customers that, obviously there are ones that are spending under $3.9 million, otherwise that wouldn't be the average to spend significantly more, which there already are customers in that cohort that are. So we're encouraged by that as well. I hope that makes sense.

And I'm showing no further questions at this time. That concludes today's conference call. You may now disconnect.

Great. Well, thank you, Dennis, for hosting us at the conference. We're pleased to be here. I'll just give a quick presentation to recap our quarter, and then we'll get into Q&A. To skip our forward-looking statements and just refer you to our disclosures, please.

So to give you a quick update on the company, we're a computational lab for molecular discovery. Most -- the application is mostly in drug discovery. So in AI, computational-based drug discovery, everyone knows it's all about the quality of the data. Where we step in is supplementing experimental data with simulation data and doing that in a way that's as accurate as the experimental data so that you have a training set upon which you can generate the computations. Many new entrants in the field are starting to embrace the simulation approach and the physics-based approach to doing this. Many of them are our customers. We're very pleased that they're seeing the approach the same that we are.

In terms of validation of our company and our platform, we have about $250 million of revenue and billions of dollars of M&A volume that's transacted around our molecules. The core of our company is a computational platform. As I referenced, we monetize this in 3 primary ways. We have a software business that's utilized by life science and material science customers. We have over 1,700 customers, as you can see there. We also monetize the platform in collaborations with pharmaceutical partners and also some material science customers. We have 19 active collaborations, which has grown over the past 12 months. And then we do internal drug discovery on our own on a proprietary basis. We have 7 active programs there.

To give you an update on the quarter that we just reported, $54 million in revenue, which grew 54% year-on-year, $40.9 million in software, which grew 28% and $13.5 million in drug discovery. We also made a number of announcements this quarter regarding our guidance. So still maintaining about $250 million of total revenue, but a slight decrease in software revenue, offset by a slight increase in drug discovery revenue. So 8% to 13% software and $49 million to $52 million of drug discovery revenue.

We also announced a transition to focus more on an R&D-based model in discovery. Our intention is to complete dose escalation studies on our 2 clinical programs and then find partners to advance the remainder of development and also that we won't seek to advance any of our internal assets to the IND stage. In terms of the effect of those announcements, we believe this will leave Schrödinger as a more focused, streamlined company with a goal towards profitability.

In terms of our pipeline, what's shown on the page here is a collection of the assets that we've worked on and generated IP. We are pleased to see the continued progression of these molecules in the hands of our partners all the way through commercialization. What's also on the page is our preclinical work in collaboration with pharmaceutical partners. Each of those partners listed there, we have multiple targets active in our collaborative efforts there.

The reason we're focused on our discovery efforts in preclinical is our track record. We've generated $600 million in the last 5 years through upfronts, the sale of companies and other milestones. If you look at what that means in our portfolio, we have 15 programs in which we are entitled to milestones or royalties. And the collective milestone opportunity there is $5 billion over the course of those programs.

To just recap our strategic priorities, we are focused on completing our dose escalation studies and presenting data on 3515 that will be presented early next year. 1505 will be presenting additional data around ASH. And then around our software platform, it's increasing the adoption with our pharma customers and rolling out new products and enhancements to the platform.

So I'll wrap up there and pleased to take questions from Dennis.

Thank you, Richie, for that presentation. Maybe just from a big picture perspective and when you look across the space, the industry, there's been a lot of enthusiasm around AI, drug discovery, et cetera. There's been many deals done from pharma, not only with you, but with other platforms as well. Can you just talk a little bit about what makes Schrödinger's platform unique and some of the points of differentiation?

Do you want to start?

Yes. Karen Akinsanya, I'm Head of R&D for Therapeutics and also Chief Strategy Officer for Partnering. So the application of computation to drug discovery is obviously not new. Schrödinger has been around for 30 years, essentially building a physics-based platform that allows you to accurately compute the properties of molecules. What's happened in the last 5 or so years is the application of machine learning to drug discovery. And that really relies on training sets, existing data that is essentially used to generate new molecules. The difference between, I would say, what we do is that we incorporate physics and that's highly accurate. You don't need a training set.

And so I think the fact that the pharma industry, our customers, all pharma companies and a very large slice of biotech use our platform. It's a professionally developed software development effort. And so I think the difference between what you're seeing now with AI-driven drug discovery is whether those methods can be used in conjunction with, I would say, the physics-based methods that are used ubiquitously now based on our platform in the industry.

Maybe talk a little bit about the overall trend for you guys in terms of the software revenue, just how that has grown over time? And is it just around enthusiasm in the biotech industry for more drug discovery to discover more unique molecules that have higher probability of success? Or is it any other fundamental drivers of your software growth?

Yes, I'm happy to cover that. In the past few years, the biotech component of our business and its contribution to growth has been challenging. That continues to be the case. As we see some early signs of recovery there, we'll look to see how that materializes. But what has driven the growth over the past few years is expanding our relationships with pharma customers. This is about they're embracing a compute-first, predict-first approach into their drug discovery organizations and increasing the licenses and seats and the adoption across their organizations of our approach. We do collaborative programs with a number of these customers, and that we see as a high correlation and a high causation to allow those customers to utilize the software on their own for their own R&D programs.

So in terms of software revenue, usually, it can be a little bit lumpy, a little bit choppy during the year. And as you think about 2026, I guess, how much visibility do you have on software revenue and on some of these contracts that hopefully will be renewed or expanded even?

Yes. Q4 is our biggest quarter for bookings and renewals. As we close out this quarter, we'll have a better sense into 2026, what the opportunities are.

Okay. And should the expectation be that you guys will continue to grow software revenue year-over-year?

Yes.

Okay. Great. And then I would love to talk a little bit more about some of these partnerships that you have with Novartis, Lilly, et cetera. How did those come to be? And I guess, does that open like do successful milestones from those partnerships open up a bigger avenue for software licensing and usage across their companies?

Yes. Maybe I can start on the partnerships. So going back 15 years, Schrödinger has been partnering around the platform and helping to invent molecules. Richie covered that in the opening remarks around our partnerships and co-founding of companies. More recently, we've been accelerating the number of partnerships we have with pharma companies. So 2020 BMS, Lilly in '22, Novartis last year.

The way these partnerships come about is essentially 2 ways. Number one, they already are using the platform, and they are interested in expanding the use of the platform. At the same time, we're socializing some of our proprietary programs with them. And usually, there's a meeting of minds around particular targets that are of high interest where we've initiated the work or where they've initiated the work and there's a joining of forces to fully deploy the platform across those programs.

And that portfolio of collaborations, as you heard Richie say, has expanded quite profoundly over the last 5 years to the extent that we now have quite a bit of upfront that's being recognized as revenue, but also milestones coming from those, but what we've also seen is companies now with this front row seat on how we use the platform, significantly scaling up their use of the platform and their spend on the technology.

Thanks, Karen. And to address the second part of your question, earning and recognizing milestones on our programs, we see as additional proof points and validation of the benefits of working with us. We also see that leading to those partners coming back and adding collaboration programs with us and expanding the relationship.

Yes. And I'm not sure what is more validating than multiple M&A of assets that spun out of Schrödinger, right?

Yes, I agree. And just one other comment I'd add is our ability to execute in discovery, if we look over a 5- or 10-year time period, we are able to generate DCs quicker than what we could before, whether it's with the collaboration partner or whether it's on our own proprietary assets.

Okay. And I guess moving on to the pipeline. Just remind us of some of the promising data you guys had in MALT1 as well as some of the data that's coming up for the Wee1 program.

Yes, sure. So I can provide an update on MALT1. This is essentially downstream of the BTK receptor controls NF-kappaB signaling. We've completed now dose escalation in our Phase I study, where we demonstrated differentiated safety, no DLTs, no deaths, essentially very strong PD data demonstrating that we have shut down NF-kappaB signaling, and that translated to early signs of monotherapy activity, 100% response rate in Waldenstrom's, relapsed/refractory to BCL2 and to BTK and also responses in CLL, mantle cell and a range of tumor types or histologies where you typically see BTK working.

So we think this is a very exciting opportunity for the post-BTK post degrader, post-BCL2 population that will be growing as people come off those drugs. So we announced, I think it was in August that we, having now completed a good portion of the Phase I study, we will be looking for partners for that program.

The second program is our Wee1/Myt1 co-inhibitor. This is a synthetic lethal play on what we know that Wee1 has efficacy where we're approaching opening up that therapeutic index because of that synthetic lethal relationship. That program is in dose escalation right now, and we plan to share data on that program in the first half of next year. So far, so good. Things are going well, and we look forward to sharing that update.

How should we think about that Phase I update in terms of efficacy and safety, PK, et cetera?

Yes, sure. So as you saw this year for EHA, we were very careful not to dribble out data. We essentially had completed our analysis of the PK/PD relationship, the safety up through dose escalation and also had gathered sufficient repeat scans on patients. And so that's the same approach we're taking with this program. We won't be trickling out data. We decided to move this to first half, so we can more thoroughly analyze the data. We will get PK data. We will get PD engagement of Wee1 and Myt1 and also preliminary efficacy data looking for, obviously, responses in solid tumors.

Okay. Have you talked about what sort of solid tumors are being enrolled in the study?

Yes. So as you know, Wee1 inhibitors have shown very impressive efficacy in uterine serous carcinoma and ovarian, but we've also been enrolling other types of solid tumors. And so there'll be a mix of data across gynecological as well as other solid tumors.

Okay. Can you talk a little bit about the biology around Wee1 and Myt1? And by inhibiting that, should we be expecting fairly strong responses? Or is it more of a cytostatic type of mechanism where maybe ORR may not be super strong, but the benefit comes from PFS or DOR?

Yes, it's a great question, actually. So the biology here is that Wee1 inhibitors are DNA damage repair compounds. And what you essentially are doing is pushing cancer cells into this catastrophic apoptosis. And so previously, it has been shown that just the DDR/Wee1 inhibitor on its own can drive stable disease, prolonged stable disease as well as partial responses in solid tumors. The issue, though, has been that safety has been a challenge. You can imagine that rapidly turning over cells in the body are also impacted by a cell cycle kinase inhibitor.

And so what we've done with our Wee1/Myt1 co-inhibitor is to take advantage of that synthetic lethal relationship where essentially Myt1 is a break on Wee1. It basically means that you need to dose higher with a single Wee1 agent. And we've shown preclinically that when you have the Myt1 synthetic lethal, you can dose up without having extreme toxicity. You can also see that there's less resistance forming to Wee1 in that synthetic lethal pair.

And so what we hope to be able to see at the end of Phase I, obviously, we'll be sharing initial data. But by the end of Phase I, what we're looking for is evidence of efficacy, stable disease, PRs, but also that we can safely dose Wee1 up in the presence of this Myt1 and that, that translates to people staying on the drug.

Yes. Okay. Can you remind us what are some of the on-target talks for Wee1?

Yes. So as I mentioned, rapidly turning over cells are going to be particularly sensitive, and that means you see cytopenias, you can see gastric disturbances, diarrhea, changes in blood cell counts, really sort of significant AEs that mean people discontinue. And so again, what we're hoping to see is that we're seeing fewer of those side effects. To the extent to which you can get rid of them, I think, is limited because these are DDR agents, but really just opening up that window that allows people to stay on drug.

Sure. And as you complete the Phase I, I guess, what are next steps after that? Is it just to go out and look for partnerships kind of in line with what you guys have been saying on Q3 in terms of this shift in strategy?

Yes, totally consistent with the strategy that was shared by Richie and we spoke about on our earnings call. each of these mechanisms, I'll say first, MALT1, we believe this is a mechanism that will combine very well with BTK inhibitors. And so across multiple histologies and in combination, that really requires a focused effort to maximize the value of the asset. And I would say the same thing on Wee1/Myt1. I think people are excited about the activity they've seen with Wee1 inhibitors. If we have a package that supports combinations, supports the opportunity to essentially have people stay on drug, not just on Wee1 inhibitors. In fact, today, a paper came out, I don't know if you guys saw it, about CDK4/6 and Wee1. We think that these are really interesting combination opportunities for partners who have such assets.

Okay. Interesting. Maybe taking a step back, can you talk a little bit more about that change in strategy of no longer going into the clinic buyer itself? What was the rationale behind it? And I know, Richie, you mentioned around profitability and cost optimization, but can you give a little bit more color there?

Yes. I think these are -- there's many factors that went into these decisions. Where what we feel is that the most synergistic elements of the business are the software licensing and the collaboration and preclinical discovery efforts. That's where we want to focus. That's where we see the growth opportunities and the combined benefits across both those pillars. We are very proud of our pipeline page and the work that -- and the track record and the progression of molecules in the hands of our partners. And we look forward to continuing that journey on our clinical programs and our clinic-ready programs.

Yes. And maybe just to say that -- just to reiterate the $600 million that we've generated over the last 5 years, that's not come from partnering clinical programs. It's actually come from partnering discovery programs. And so the thesis 5, 6 years ago that we could essentially generate that much value from discovery partnerships was unclear. I think we've clarified that now, and we would be able to generate value without necessarily the risk and the spend of clinical development.

Right. Because the preclinical drug discovery aspect is kind of shorten your secret sauce, right, not necessary clinical development and execution, which obviously takes a lot of capital and you guys are conscious about the cash runway and the spend, et cetera, over the long term.

Yes. But not giving up the upside, right? The important thing is the milestones, the royalties. We have royalties on 15 programs already in that table that continues to grow. So that gives us future opportunity for value.

Should we be expecting additional pharma partnerships over the next 12 to 24 months, similar to kind of like the Novartis deal, et cetera? Like just -- I'm just trying to gauge the amount of interest and enthusiasm right now and some color around the discussions that you guys are having.

I mean I think we want to be careful not to guide to any specific type or cadence of BD deals, but it's been such an important piece of our story over the last 5 -- actually last 15 years that doing deals, partnering, I think, is going to remain a key part of our fabric. And so yes, you should expect more deals when they are and who they're with. I think we will share when the time is right.

Sure. When we think about the platform, it seems like it's very therapeutic area agnostic, right? Is that fair?

Yes.

Is there -- as you guys go through some of these target identifications and picking the right targets to go into, I guess, tell us a little bit about your thought process on figuring out which one may seem the most derisked and which one may give the platform an edge in terms of actually coming out with a very promising molecule.

I can start. I mean I think the first thing to say about being therapeutic area agnostic is it gives us a lot of diversification. If you look at that pipeline page, we're in immunology, we're in oncology. We're in a lot of very significant disease areas, which is great.

In terms of how we think about picking targets, the human evidence component is critical. We think there's a lot of risk in taking novel targets that have only been validated in preclinical models. And so we incorporate our understanding of clinical data, human genetic evidence. But most importantly, because we're a structure-based drug design company, it's really around what do we understand about the structure of that protein, the molecular structure function link and how that links to pathophysiology.

And so that's something that we've built up a really strong capability in. And it's actually one of the things that led to the Novartis deal. We had what we believe to be the first structure for that target and a really good understanding of how to drug it in a very large chronic disease indication. And so we intend to do more of that, combining all of these different data sets that we don't necessarily generate, but that we synthesize and think about how to overlay that on novel structures.

Sure. In the last 1 or 2 minutes, maybe just remind us of your cash, some of the optimization efforts that you guys have announced as well as the runway.

Yes. We're ended the quarter at $401 million in cash. This year, we've made 2 significant actions. In May, we had a $30 million expense reduction program. In 2025, we'll recognize more than half of that. The balance will come next year. And then the collective effect of identifying partners for the clinical programs will result in about a $40 million saving once we've wound down those programs and transferred them out.

So the net effect of those things will obviously improve our operational profile as well as our profitability profile. We're not concerned about runway, but obviously, those changes will extend the runway as well. So we feel very well capitalized to kind of run the business in front of us.

Great. Well, thank you so much for being here. It's great seeing you. I'm really looking forward to 2026.

Thank you.

Thank you.

Thank you for standing by. Welcome to Schrodinger's conference call to review third quarter 2025 financial results. My name is Rob, and I'll be your operator for today's call. [Operator Instructions] Please be advised that this call is being recorded at the company's request.

Now I would like to introduce your host for today's conference, Ms. Jaren Madden, Chief Corporate Affairs Officer and Head of Investor Relations. Please go ahead.

Thank you, and good afternoon, everyone. Welcome to today's call during which we will provide an update on the company and review our third quarter 2025 financial results. Earlier today, we issued a press release summarizing our financial results and progress across the company, which is available on our website at schrodinger.com.

Here with me on our call today are Ramy Farid, Chief Executive Officer; Richie Jain, Chief Financial Officer; and Karen Akinsanya, President, Head of Therapeutics R&D and Chief Strategy Officer, Partnerships. Following our prepared remarks, we'll open the call for Q&A.

During today's call, management will make statements that are forward-looking and made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, including, without limitation, statements related to our financial outlook for the full year 2025, our plans to accelerate the growth of our software business and advance our collaborative and proprietary drug discovery programs, the timing of and initiation of and readouts from our clinical trials, the clinical potential and properties of our compounds, the use of our cash resources as well as future expenses.

These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies, and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially due to a number of important factors, including the considerations described in the Risk Factors section and elsewhere in the filings we make with the SEC, including our Form 10-Q for the quarter ended September 30, 2025. These forward-looking statements represent our views only as of today, and we caution you that, except as required by law, we may not update them in the future, whether as a result of new information, future events or otherwise.

And with that, I'd like to turn the call over to Ramy.

Thank you, Jaren, and thank you, everyone, for joining us today. We made very solid progress during the third quarter. Total revenue was $54 million, a 54% increase from the third quarter of 2024, reflecting strong execution across our business.

Software revenue in the third quarter was $40.9 million, representing 28% year-over-year growth and was just above our expectations. Drug discovery revenue was $13.5 million, highlighting the progress in our collaborative programs.

We are seeing continued strong demand for advanced computational solutions across the industry. We are also pleased to see wide recognition that simulated data is required to realize the full potential of AI and drug discovery.

To effectively harness AI and machine learning for molecular discovery, vast amounts of high-quality physics-based simulation data are essential for training robust AI models. Experimental data alone is insufficient to generate the required training data.

Schrodinger's differentiated and extensively validated platform generates high-quality simulated data at a scale that far exceeds what is possible with experiments alone. With this new computational physics plus AI paradigm becoming the accepted standard, we are very optimistic about the long-term potential and value of our platform.

As we execute through the remainder of 2025, we are encouraged by the continued high level of customer engagement as the macroeconomic pressures that have impacted the industry stabilize.

While we remain confident about our long-term growth opportunity, we are updating our software revenue growth guidance for 2025 to 8% to 13% from 10% to 15% to reflect our current expectations regarding the timing of certain pharma scale-up opportunities.

Turning briefly to our pipeline. We continue to work toward completing the Phase I package for SGR-1505, our MALT1 inhibitor and the Phase I dose escalation study for SGR-3515, our Wee1/Myt1 co-inhibitor.

Beyond these planned investments, we do not intend to advance our internal discovery programs into the clinic independently. This decision and the $30 million expense reduction in May improve our operational efficiency and long-term profitability profile.

We are continuing to invest in advancing our platform, including making significant improvements to the accuracy and domain of applicability as well as usability, which is driving adoption among scientists throughout the R&D organization, not just dedicated computational chemists.

Last week, we released our 2025-4 software update, which includes enhancements for challenging modalities such as bifunctional degraders. Additionally, the beta for our predictive toxicology solution is ongoing. This version encompasses approximately 50 representative kinases in addition to multiple key anti-targets. We are continuing to expand the number of off-targets supported in our platform and are optimistic about the potential long-term contribution of this product.

Overall, we have made considerable progress this year and remain focused on executing against our strategic priorities, including increasing customer adoption of our software, delivering major scientific advancements to the platform and advancing our therapeutics portfolio.

I will now turn the call over to Richie to discuss the financials in greater detail. Richie?

Thank you, Ramy, and good afternoon, everyone. Schrodinger had an excellent third quarter with strong growth in both software and drug discovery revenue, coupled with disciplined expense management.

Total revenue for the quarter was $54.3 million, an increase of 54% compared to Q3 2024. The increase was driven by both higher software and drug discovery revenue. Software revenue was $40.9 million, an increase of 28% compared to Q3 2024 and just ahead of our expectations for the quarter.

The increase was primarily driven by higher revenue from hosted contracts, on-premise renewals and contribution revenue from the grant related to our predictive toxicology initiative. This growth primarily reflects the expansion of existing accounts with limited contribution from new customers.

Drug discovery revenue was $13.5 million compared to $3.4 million in Q3 2024. The increase reflects continued successful execution across our expanded portfolio of collaborations. Software gross margin for both Q3 2025 and Q3 2024 was 73%.

R&D expenses were $42.8 million in Q3 2025, a 16% decrease from $51 million in Q3 2024. The decrease was primarily due to lower employee-related expenses and the continued shift of the predictive toxicology expenses into software cost of goods sold from internal R&D.

Sales and marketing expense was $9.5 million, an 8% decrease compared to Q3 2024. G&A decreased 13% to $21.7 million. The decline in both expenses was primarily due to lower employee-related expenses.

Overall, total operating expenses were $74 million in the quarter, a decrease of 14% compared to Q3 2024. Total other income was a gain of $13 million compared to a gain of $30 million in Q3 last year due to mark-to-market changes in our equity investments and currency fluctuations.

Net loss was $33 million or $0.45 per diluted share versus a net loss of $38 million or $0.52 per diluted share in Q3 2024. The fully diluted share count for Q3 was 73.6 million compared to 72.8 million in Q3 2024. We remain well capitalized with $401 million in cash and equivalents as of September 30.

Turning to our full year software guidance. We are updating our revenue growth and gross margin expectations for the year. We now expect software revenue growth to be in the range of 8% to 13% compared to prior expectations of 10% to 15%. This change is driven by the slowdown in pharma discussions resulting from the multitude of factors impacting the industry and our relatively long sales cycle for scale-up opportunities. We are having positive conversations with customers and our scheduled renewals remain on track.

While we may experience certain delays this quarter, we remain confident in the long-term potential for growth as industry pressures lessen. We are encouraged by the early signals of recovery in the biotech sector, including in the capital markets, M&A and new capital formation, creating additional opportunities.

We are addressing the industry's increasing demand for agentic integration and R&D efficiency as well as expanding the domain of applicability across the drug discovery and pre-clinical development continuum. Collectively, these provide additional opportunities for us to demonstrate value to our customers and access additional budgets.

Shifting to the remainder of our guidance. We are pleased with the progress we have made across our collaborative portfolio and have increased our drug discovery revenue guidance to $49 million to $52 million, which slightly exceeds our prior expectation of $45 million to $50 million.

Software gross margin is now expected to be 73% to 75% versus 74% to 75% previously, reflecting the change in software revenue expectations and our relatively fixed cost structure for software cost of goods sold.

We are committed to managing our expenses and our expense guidance remains unchanged. We continue to expect operating expenses to be lower than 2024 and cash used in operating activities to be significantly lower than 2024.

Our headcount is now appropriately sized to achieve our business objectives after the $30 million expense reduction announced in May. We have already realized more than half of the $30 million savings and the remainder will be realized in 2026.

This action, plus the phasing out of independent clinical development activities and associated reduction in team will provide savings of approximately $70 million and improve our long-term profitability profile.

Overall, we reported strong financial results for the quarter. Our business is resilient, and we are committed to taking advantage of the opportunities in front of us.

With that, I'll turn the call over to Karen to discuss our therapeutics R&D and pipeline updates.

Thank you, Richie, and good afternoon, everyone. Our highly experienced drug discovery team are pioneers of the predict first computational approach to drug discovery. We leverage structural biology breakthroughs and physics combined with the power and speed of AI to enable broad exploration of chemical space to identify novel molecules that repeatedly meet a wide range of product profiles.

Examples of molecules discovered as part of collaborations include zasocitinib acquired by Takeda from Nimbus MORF-057 now advancing at Lilly following the Morphic acquisition and Structure Therapeutics, GSBR-1290, which all continue to make progress through the clinic. These Phase IIb and III assets represent the most advanced examples of medicines designed by leveraging large-scale use of our physics-based methods, along with AI and machine learning.

Turning to updates on our clinical pipeline. Next month, we will present new translational data and a clinical update on SGR-1505, our MALT1 inhibitor during a [ poster ] session at the American Society of Hematology Conference.

The abstract published on Monday builds on the encouraging data we presented in June, reinforcing SGR-1505 as a potential best-in-class MALT1 inhibitor for the treatment of relapsed/refractory B-cell malignancies in patients who become resistant to standard of care agents.

The abstract includes initial data in patients with aggressive lymphomas such as ABC-DLBCL, where one patient has now achieved a complete response as well as updated safety and efficacy data in patients with Waldenstrom's macroglobulinemia or CLL. The poster will also include translational data on the mutational profiling of BTK and BCL2 inhibitor resistance mutations.

These data, combined with the recent orphan drug designation by the FDA in Waldenstrom's, support the therapeutic potential and commercial opportunity for SGR-1505. We are continuing to focus on securing the right strategic partnership to ensure this program receives the dedicated focus and resources required to pursue mid and late-stage development, and we are encouraged by the conversations we have had to date.

Moving to SGR-3515, our Wee1/Myt1 co-inhibitor. We are currently focused on completing the Phase I dose escalation study in patients with advanced solid tumors. We are encouraged by the progress to date based on our preliminary review of safety, PK and PD and now expect to share initial clinical data in the first half of 2026, which allows us more time to fully analyze and assemble the Phase I data for 3515.

Last month, we presented pre-clinical data for SGR-5573, our potent selective brain penetrant inhibitor of osimertinib-resistant EGFR variants at ESMO. The data demonstrated that SGR-5573 is potent against resistant EGFR variants, has strong wild-type selectivity and robust antitumor activity in pre-clinical brain metastases models.

Additionally, we recently selected a development candidate in our NLRP3 program. SGR-6016 is structurally distinct from other known NLRP3 inhibitors and has several potential best-in-class attributes, including brain penetrants and an encouraging pre-clinical potency selectivity and safety profile.

Others have recently demonstrated clinical proof of concept for NLRP3 as a potential treatment for patients with cardiovascular risk factors and obesity. We have advanced more than 25 programs to the development candidate stage, either independently or through collaborations since establishing our therapeutics team.

Since 2020, we have generated approximately $600 million in cash from companies we have cofounded or from our program licensing and collaboration activities. We intend to build on this track record by continuing to leverage our extensive combined expertise in structural biology, functional insights, and the full-scale use of our platform to unlock high potential target product profiles.

With approximately 15 programs currently eligible for future milestones and royalties from our past activities, we believe a discovery-focused therapeutics R&D model has the potential to deliver additional long-term value and significant returns through licensing, new ventures, and discovery collaborations.

As we wrap up 2025, we look forward to sharing our SGR-1505 update at ASH and to advancing our early-stage and collaborative portfolio. We appreciate all of the hard work of our discovery and development teams who have enabled our progress to date and future opportunities.

I will now turn the call back to Ramy.

Thank you, Karen. We have made significant progress across the business this quarter, and we are optimistic about our outlook through the end of the year.

Looking ahead, we are operating at the intersection of 2 powerful currents shaping the future of molecular discovery, the integration of computational drug discovery and the industry's dramatically increased focus on AI. We are at the forefront of this paradigm shift.

We believe our technological advantages, combined with the strategic actions we have taken to improve our operational efficiency and long-term profitability profile, will position us to deliver growth in the years to come.

At this time, we'd be happy to take your questions.

[Operator Instructions] Your first question today comes from the line of Mani Foroohar from Leerink Partners.

A question about the implications of the guidance regarding the reduced spend year-over-year and trimming of OpEx. How should we think about that in line with your commentary around reduced focus on novel clinical development, et cetera? What does that imply in a longer-term time horizon about how we should think about your OpEx trajectory?

Yes, Mani, thanks for the question. I think we've -- just to summarize the actions we've taken. We announced a $30 million expense reduction in May. We've achieved more than half of that goal. We'll achieve the full amount by next year. The announcements that we've made today regarding our clinical intentions, that has the effect of another roughly $40 million. That is putting us on a track towards improving our profitability profile. We're not guiding to any specifics there, but these are actions that we've taken to improve the overall profitability profile that we're on.

Okay. And as a quick follow-up, [indiscernible] got the one. Do you guys think of formal profitability either in GAAP or cash terms as a meaningful milestone to pursue? Or is that not a metric that you guys think is really meaningful on its own?

Yes. It's a meaningful milestone for sure, and we're taking actions towards that goal. I think, again, another way that we're thinking about this is the -- we went public in 2020. We've not raised external capital since then. There was a follow-on in 2020, but nothing since then. We've been incredibly productive in growing the business between software revenue and drug discovery revenue. And we've had an incredibly productive business development effort in our discovery programs, that's generated $600 million of cash over the last 5 years. So we are focused on longer-term profitability and improving the profile. And these are the actions that we're taking towards that goal.

Your next question comes from the line of Scott Schoenhaus from KeyBanc Capital Markets.

Nice quarter. Question on the software guidance here. You've been reporting pretty nice software growth. Third quarter was really strong, 28% growth. And there was comments about sort of seeing a slowdown in the end markets with your discussions with your customers. Maybe talk about what's changed over the last -- or maybe when this started -- the slowdown started to happen? And maybe by cohort where you're starting to see the slowdown happen on the software side?

Yes. Thanks, Scott. So yes, as you mentioned, we're pleased with the quarter, the quarterly results, and the year-to-date results with software growth of roughly 30%. As you know, there's been a multitude of factors impacting the whole pharmaceutical industry this year.

Despite that backdrop, we've been encouraged with the continued high level of customer engagement and the initial signals that suggest that the industry is stabilizing and poised for a recovery. But I think we have to be honest that sustained improvement in the sector may take time, and we are cautiously optimistic.

With that in mind, we did lower the software guidance by 2% at both ends of the range to reflect the uncertainty regarding the timing of certain pharma scale-up opportunities. It's worth noting that our scheduled renewals continue to be on track.

What's changed since our last call in August is the conversations that we've been having with our customers regarding scale-up opportunities have been delayed longer than anticipated.

As these conversations matured or have matured, we have greater visibility into the size of the opportunity, but less visibility into the timing of close. We also did not anticipate the continued challenges in the biotech sector. We did not factor that into our revenue guidance growth for the year, but some of the challenges have been greater than we expected and have persisted over the previous few months.

The underlying fundamentals with those customers have been weaker. You've probably seen all the news that we have been seeing with layoffs and companies altogether shutting down discovery or not achieving anticipated financing rounds.

Even recently, there's been some very high-profile companies that have shut down their operations altogether as an indication of continued challenges. So we are seeing some early signs of recovery with biotech as well, some new customer formation that are creating opportunities for us. But across pharma and biotech, those are the tops of the waves that we're seeing since our last call in August.

That's super helpful. And then I guess a follow-up question would be on the predictive toxicology. I know it's still in beta. How is the customer response to it? When do you think you can start monetizing? And I know, Ramy, you said it's more of a longer term opportunity, but maybe if we could just like provide more color there on the timing of the monetization of this product?

Sure. Yes. So first of all, we and actually the Gates Foundation are who funded the work, are quite pleased with the progress we've made. It's been really going extremely well. There's also really significant interest in the project and the initiative and in the ultimate product, the ability to actually predict toxicity associated with binding to off targets. And we're engaged in quite a number of discussions with customers.

And as you noted, we are actually, yes, still in the beta. There are customers using it, but it's a little early to discuss feedback yet. So we're not quite there yet, but we're really, really pleased with the interest and the progress we're making both in the product itself, but also with the beta.

Your next question comes from the line of Matt Hewitt from Craig-Hallum Capital Group.

Maybe just to dig in a little bit about your expectations of no longer advancing discovery into the clinic. Does this mean that you'll still be seeking and finding new molecules, but rather than advancing the clinic and then looking for partnerships, now you'll be looking for those partnerships pre-clinic? And what does that mean from an economic standpoint?

Yes. Thanks for the question. I think that we can just affirm that we are indeed continuing to work on discovery stage programs, new ideas, new programs as we have been doing actually since the inception of the therapeutics team here at Schrodinger.

In terms of our expectations about when one would start to consider partnership, I want to reference the work that we've been doing over the last 5 years to essentially socialize programs from their very inception.

Last year, you will recall that we partnered a program that was at the very early stages of drug discovery with Novartis for very significant economics. That was a $150 million upfront deal. It obviously allowed us to partner with a company that ultimately will take those programs into the clinic, but to do the discovery and sync with them so that not only are we very aligned on the target product profile and the features of the molecule, but they're at the same time learning about our platform as they go to adopt that system-wide. So this has worked very well.

You heard Richie say that we generated $600 million over the last 5 years from this model. And so we're very confident that we can continue to generate value while working on a very diverse and broad range of targets in the discovery space.

Very helpful. And maybe my follow-up. It sounds like the renewals are on track, you're having success there, but you did note that you're still -- that you're struggling a little bit on the new logo or the new customer front. What do you think -- you also mentioned that you're seeing some improvement in the macro. What do you think it's going to take to kind of flip over where you are starting to sign new contracts with new customers to drive some incremental growth?

You want to take that?

Yes. Thanks for the second question, Matt. Yes, we -- I think the growth that we've delivered this quarter and mostly year-to-date has been growing within our existing customers and closing adoption gaps and scaling up relationships.

As we think about new customers, new logos, the biotech market we're seeing, I'd say, are encouraging early signs. Every biotech has its own features. I think we're still trying to figure out how this one is going to come together and what opportunities that will open for us. There -- While there's encouraging signs, there's also discouraging signs with customers going out of business.

So, there are some new capital formation opportunities that we think are exciting, that we are having nice conversations around. We are -- we need to advance those to close, and that can give us some greater visibility into those opportunities.

Your next question comes from the line of Evan Seigerman from BMO Capital Markets.

This is Conor on for Evan. I just maybe had a follow-up to the delay that was announced for 3515 today. I was just wondering if maybe you could expand a little bit on what occurred there and kind of maybe when we should be expecting to see data in the first half of '26?

Yes. So, similar to what we did when we were preparing to share data on our first program, SGL-1505, our MALT1 inhibitor, we've elected to complete the collection and analysis of data related to PK safety, PD and preliminary activity before providing an update on the ongoing trial.

And just because of where we are in that cycle of data collection and analysis, we decided to move that over into the first half of 2026. We have not yet confirmed the venue or the exact timing of that, but we do expect that to be in the first half, potentially at a medical meeting.

Your next question comes from the line of Sean Laaman from Morgan Stanley.

This is Morgan on for Sean. Wondering if you could share any more details about the SGR-6016 NLRP3 inhibitor and any plans there in terms of what you're doing to progress that?

Yes. We're very excited about this compound. We haven't talked a lot about NLRP3 program. I think we covered it [ at ] Pipeline Day 1 year or so ago. We have selected a development candidate that has really impressive properties. We predict this to be a very low-dose drug, which we think set it out to be an excellent candidate for combinations.

And it is a brain-penetrant NLRP3 molecule with pretty impressive properties there. We used our Esol capability to really optimize that brain penetration. So, a really nice looking molecule, preliminary talks are underway. And in terms of the plans for this molecule, it's kind of interesting and timely given the recent updates in the NLRP3 space. We have been socializing this program with potential partners, and we'll update you as those discussions progress.

As you heard Ramy point out on his -- in his remarks, we won't be taking this forward alone or ourselves. We'll be doing that in the context of a partnership. And so again, we'll update you once we have more information about that.

Your next question comes from the line of Dennis Ding from Jefferies.

The decision to not do more clinical work on your own, I guess, why now? And is there any read-through to the Wee1 data you guys are seeing so far?

I'm sorry...

Can you repeat the second part of the question? Was it about Wee1 or -- what was the second part?

Yes. I mean, given the Wee1 and Phase I dose escalation, I'm just curious, this decision to not move things further into the clinic on your own, just the timing and just why now and why not wait a little bit more before making that decision seeing the full set of that data?

I think there are multiple answers to this question. So I'll invite Ramy and Richie to comment as well. I think you heard us explain on the call here to others that we have been extremely productive and successful at partnering programs that are still in discovery and generating -- are still in discovery or completed discovery where we're collaborating already with others.

And so we think that is a very high potential model for us to continue without exposing ourselves to having to generate clinical data ourselves. So this is really not related, let me be clear, to our assessment of SGR-1505, our MALT1 inhibitor, which we're very excited about. And we've already announced that we plan to progress that in partnership with others, or with respect to 3515, as I just explained to you the prior -- on the prior question, we're still gathering that data. We expect to have a more complete analysis in the coming months.

And you heard, we also talked about NLRP3. We're excited about all of these programs. We just think that Schrodinger's profile right now, It's much better to advance those molecules in the clinic with others and expand on what we've done in discovery to create value.

And I'll just add, as we've been saying since the IPO that we're very excited about the synergies between drug discovery business and the software business. And we see the height of those synergies in the discovery efforts. And that's why we're really focused on the discovery efforts. You heard earlier in -- both in our remarks how successful that is. I think Karen has said this, it's very obvious that this is something that we should be investing more in.

Perfect. And then as a follow-up, can you just please give an update on the Novartis partnership and the progress that has been made there since I mean, we've almost anniversaried the announcement of that partnership?

Yes, you're right. It's about 1 year since we announced. Excellent progress across the efforts there. Teams are working extremely well together, both on the advancement of the programs, but also on the incorporation of the [indiscernible] Schrodinger's platform into the work that Novartis are doing. You can tell from the revenue update that a portion of that is related to the Novartis progress. And so, happy to report all is going well, and we're looking forward to another productive year with Novartis.

[Operator Instructions] Your next question comes from the line of Andrea Newkirk from Goldman Sachs.

This is [ Twana ] on for Andrea. Just a quick one from us. Would you mind elaborating some more on the nature of the ASH disclosures for SGR-1505? Will those new data feature the same patients mostly from the prior analysis with some additional follow-up time? And what will be the most key takeaways from those data in your view?

Yes, absolutely. So if I could just take you back a moment to ASH -- sorry, to EHA. So at EHA, we provided our first update on SGR-1505. At that time, we had really only focused on a lot of the analysis around the indolent patients with CLL, WM. That was based on the FDA recommendation to delay dosing aggressive patients.

So there's a couple of key updates, I would say, from the abstract that came out yesterday, that we'll be presenting at ASH. The first is an update on the aggressive patients. If I can remind you, our patients with DLBCL are in that category. If you have had a chance to look at the abstract, you will see there, and I can update you now, but we now have patients with a complete response in the aggressive space. This is -- let me remind you, monotherapy MALT1 inhibition producing a complete response in an aggressive lymphoma patient. We think that's an exciting update, and so we'll be providing a fresh cut of the data across both indolent and aggressive patients at the ASH meeting.

The other focus of that abstract which we think is very important is the concept that patients who are double exposed, who've seen a BCL-2 inhibitor or a BTK inhibitor or both. The question was what is happening with respect to the resistance profile. So we've now done genomic profiling of several of the patients that have that particular profile.

And what we're seeing there is that they do indeed have the sentinel mutations in both BTK. I'm going to pause there because I think I was about to [ say ] something that's in the actual [indiscernible] and I won't do that yet. But they do have the sentinel mutations that you expect to see in patients who have more aggressive disease and more difficult to treat disease.

We think that is a endorsement of our idea that MALT1 is going to be an important medicine for patients with unmet need in lymphoma. And so that's the update that we'll be providing at ASH.

Your next question comes from the line of Brendan Smith from TD Cowen.

I wanted to ask maybe just another one on the predictive [ talk ] about Flare, and really how we should be thinking about the broader commercial rollout for this kind of relative to your existing customer base? Fully appreciate it's early, but I guess, are you expecting this to be maybe by and large, additive to people already using your other software? Or maybe just given where some of those users sit on different development teams, would you expect kind of separate customer population, even maybe within the same company is ultimately the prime target? And do you think that would maybe require a separate sales strategy or push? Just kind of trying to understand assumptions about who's likely to use this really out of the gate?

Yes, that's a great question. We see sources of growth in both those areas. Certainly, our existing customer base are interested in this, and it would be an add-on. This would require them actually spending more money. This is not something that would just get thrown in the package. But you bring up a really good point that we also believe this will allow us to tap into new budgets to the extent that this is obviously a technology that's of interest to toxicology groups, and that's not who we're traditionally selling our software to. We're traditionally selling our software to earlier discovery teams, computational chemistry teams. So we think we'll see growth in both those areas.

Your next question comes from the line of Michael Ryskin from Bank of America.

Apologies if I missed this, I've kind of been bounced around a couple of calls. But I want to go back to the sort of the phasing out of the independent clinical development activities. I got some of the notes from your earlier answers on that topic, but I just want to dig into it a little bit deeper.

One question is I'm curious of, is this something that you came to, based on your experience with either MALT1 or Wee1, where you're trying to monetize those assets and you just kind of didn't have the interactions with potential sponsors and partners that you thought you would and you decided it's not worth the risk?

Is this something based on sort of how much leverage you can have, how many individual programs you could push forward if you are just doing partnering out at discovery? Maybe talk about the bandwidth and the opportunity to sort of just think about number of programs you can bring forward using this new strategy shift. And I've got a follow-up.

So, I can start and just reiterate that the decision we've made today or that we've announced today is really nothing to do with the experience that we had developing our first 3 programs. The development went well, actually moved very quickly. So from idea to end of Phase I was a 5-year window for discovering the molecule and also doing the development for 1505.

So, I will say the environment for development, particularly of oncology programs, has become quite challenging. And I think some of Richie's remarks about the biotech space and the risk profile of taking programs into the clinic, this is not unique to us.

And so, I think the team has been very productive. You just heard we've got to EGFR and also our NLRP3. It's really a sustainability question, right? How many things can we put in the clinic one after the other and also reach the goals that you heard Richie talking about with respect to operational efficiency and our profitability.

So the other piece that you asked about is we have been very productive at partnering programs during the discovery phase. And in fact, if we can partner programs during early discovery, the bandwidth question you asked is we can work on a lot of programs, obviously, in the early phases of discovery. That funnel narrows a little bit as you go a little later. But there's only so many things we can put in the clinic.

And so when we looked at the overall mix of value creation in the last 5 years, we convinced ourselves that actually discovery partnerships has been very value creating, and we can continue to do that and scale that up.

Yes. So this is more about the success of the discovery program. To the extent that we can't do everything, we have to prioritize. It's very clear what we should be prioritizing. So I hope that's clear.

Okay. Okay. Yes. That's helpful. And if I can -- maybe if I can --

Maybe just one other remark.

Yes, go ahead.

The value we're generating in these programs, ultimately, we have milestones in the clinic when our discovery work is done. We have royalties on sales for those drugs that actually make it, and there's 15 of those right now. So the value creation continues even if we're not the ones doing the clinical development. There's the opportunity at least [indiscernible].

Yes. Yes, of course, that was -- Karen, that was actually going to be my follow-up question, was exactly on that. When we think about these programs now -- okay, so shift to exclusively discovery stage partnerships, things like that. When I think about these programs going forward, realizing that each one is going to be custom, it's going to be very unique. In terms of the royalties, the milestones, the economics, are they going to be similar to the ones you're doing already with Novartis and Bristol? Or are they -- is there any change to the economic structure there? Or is that pretty consistent?

Yes. What I'd say about that is -- we talked about this before. We -- as you know, we've done quite a number of these collaborations. And I think we've said this a number of times before that the economics that we've been able to demand as our track record continues to improve and the efficacy of the platform improves and the expertise of the team, the economics continue to get better. That's been the case with every program.

Now are we saying for sure guarantee the next collaboration will have better economics? Of course, we're not saying that. But I think it's noteworthy that the terms continue to improve. And that certainly would be an expectation.

I am showing no further questions at this time. That concludes today's call. You may now disconnect.

Good morning, everyone, and welcome to Morgan Stanley's Global Healthcare Conference. I'm Sean Laaman, Head of U.S. Mid-Cap Biotech Equity Research here at the firm. Before we commence, for important disclosures, please see Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. And if you have any questions, please reach out to your Morgan Stanley sales representative.

For this session, we have from Schrödinger, CEO, Ramy Farid; CFO, Richie Jain; and President, Head of Therapeutics R&D and Chief Strategy Officer & Partnerships, Karen Akinsanya. So welcome to the 3 of you.

Thanks for having us.

You're welcome. And maybe just to commence, we have some macro questions that we're asking all of our companies. And the first one is with China's rising biotech innovation, how are you thinking about Schrödinger's competitive position? And will this influence your R&D and business development strategy going forward?

Yes. Yes. It's hard to say that it won't, right? It's a real thing. I'd say, first, our technology, our platform was designed essentially to allow discovery of novel molecules, differentiated molecules. There isn't a reliance on knowledge of existing molecules. That's what -- that's a severe limitation of sort of machine learn based methods that are solely based on machine learning.

So to the extent that our platform is built on first principles and our physics-based methods, it allows the discovery of highly differentiated molecules that can solve challenging design problems. And I think in an environment like we're in that you just described, that's particularly attractive to our customers, our partners and even our own internal programs. So that's -- I think do you have anything to add?

I just came back from China. We went there to sort of survey the landscape for ourselves. And I think that there's opportunities. First of all, people view Schrödinger's platform as the gold standard and that's what we kept hearing. And as those companies evolve to working on more novel targets and globalized, I think having relationships with those companies is something that is important for us to do just with our whole customer base.

Absolutely.

Wonderful. Great response. As an AI tech-enabled biotech company, can you describe the key ways your platform is leveraging AI and think about AI's future disruption potential?

Absolutely. So first, a sort of general comment needs to be made. AI and machine learning are only as good as the training set that they're trained on. That's what makes the model effective. So small training set or a training set that's not representative of the problem, machine learning models aren't very effective. But a large training set that represents the problem well and thoroughly, AI is very powerful. And we see many examples of that. That's the case also in chemistry and design of molecules.

Now it turns out that if you take all the experimental data that's ever been generated by every company and you combine it, that's still the equivalent of a drop of water in the ocean where the ocean represents chemical space. So we don't have a lot of data to train on for chemistry. We have a lot of data for large language models. We have a lot of data for protein structure prediction, but for chemistry, there's not a lot of data. So we've developed methods using first principles, using physics that can produce experimental data, essentially, the equivalent of experimental data. But on a scale of course, that's many, many orders of magnitude faster and cheaper than experiment. So here's what we're doing. Here's how we're leveraging AI.

We are building massive training sets using first principles, using physics. And that is essentially amplifying machine learning, right? Because, again, you need a training set. So we can generate actually in 1 day the equivalent of about 10 years worth of experimental data. I mean that's extraordinary and obviously, a fraction of the cost.

So with these massive training sets now, we're seeing how machine learning and AI can have a really big impact by amplifying these physics-based methods. Now there's another area sort of different, which is you hear a lot about Agentic AI, right, agents. That's another really important area because these technologies that we're developing are new. They're complex. They're complicated technologies, and there's a shortage of people that actually -- that are experts, that can be able to run these. But there's no shortage of chemists and biologists on research teams.

So we're also developing something completely different kind of application, developing workflows, automation agents to amplify humans to be able to run these kinds of sophisticated technologies more efficiently.

Sure. It's super exciting.

Yes, very.

Yes. Last question on macro side before I'll get Schrödinger specific. But what's been the most impactful on your company from the regulatory side, if anything, is it FDA, MFN, tariffs, like...

Yes. I'll say something and then I'll hand it over to Karen. I mean we're pretty excited as I'm sure you all are too about the FDA's request demand for developing computational tools for reducing animal testing. How is that done? Well, by designing safer molecules, right? So you don't have to test as many -- do as many tests and have as many failures and you put safer molecules into animals. So we're really excited about our, what we call, predictive tox initiative. It's funded by the Gates Foundation, with a rather generous grant. And we've made great progress. We actually released the beta recently.

So that whole -- that announcement of the road map from the FDA has really been impactful in generating a lot of interest in computation. And that obviously benefits us tremendously. I don't know if...

Yes. I think in the near term, that's one of the most specific things. The FDA is clearly embracing AI and computation across the whole gamut from predicting safety but also in how they operate as an agency with respect to drug development. So those are welcome changes. Beyond that, I think a lot of these other topics don't necessarily impact Schrödinger directly.

Yes. That's right.

Sure, sure. I think we started last week, was it this week? Last week, publishing a regular AI publication as that first one. We've called it looking for my mom, which is maximally optimized molecule. So how does your physics-based platform combined with AI, machine learning, accelerate drug discovery? And what makes it scalable across programs?

Yes. I touched on that, but let me elaborate a little bit more. Again, as I said machine learning requires accurate training sets. So we can use the physics-based methods to generate accurate data sets that are on a scale that actually makes machine learning interesting, and that's accelerating drug discovery. It's resulting in getting to development candidates more rapidly, obviously, more efficiently, but also with higher quality molecules because we're able to explore way more chemical space. Now how is it scaling? The way we license our software is by the number of calculations that you can run. And that's tied directly to the number of molecules that you can explore computationally.

So of course, if you have more licenses, you scale it, you can explore more molecules, generate larger training sets, the larger the training set, the better the ML model. The more molecules you explore, the more likely you are to find a molecule that has the properties that are required to be an efficacious, safe drug. So that's how -- I think that's the answer to it.

Yes. Maybe just one other thought here is I think we're looking forward as to how computation and AI-related approaches impacting quality of molecules. I think Schrödinger is in a very interesting place because as a company that's been in this space for 30 years, there are now molecules that are so much more advanced. We've got the Morphic molecule acquired by Lilly for $3.2 billion, the whole company, obviously the TYK2. So there are a lot of proof points about the impact of competition to look back on as well as to look forward to.

Absolutely.

Sure. What are the most compelling examples of platform validation from your proprietary and collaborative pipeline?

Yes. Karen. Go ahead, Karen.

Well, I just started talking about, obviously, the 2 big acquisitions of molecules. But actually, there's 15 molecules that have entered the clinic. Some are not disclosed. Obviously, they're from our collaborations with big pharma. And then we ourselves this year, published the second MALT1 inhibitor to go into the clinic showing that we have a very differentiated profile that was optimized using our platform, took us 10 months to find that compound and we only synthesized 80 molecules. And so those stories of the work we've done with our equity partners, Nimbus, Morphic, Structure, whole host of these companies and now with the programs that we're working on, not just with new collaborators, but our own pipeline as they move forward. I'll just point to the deal that we did with Novartis last year. While the target is not known, we can't tell anyone what the target was, that is, I think, another example of the company putting up $150 million because they thought we could help them win.

Sure. Wonderful. And maybe just to go back and double down a little bit on predictive tox. What feedback have you received from data testers so far?

Yes. We just released the beta and we just started to have customers starting to use it, beta testers, our partners, close partners. We obviously work with companies that we're very, very close to or we can rely on their feedback, and they won't punish us too much if there are little issues there here and there, which of course, there always are with betas, mostly technical things, just mechanics of running it. But we haven't -- we're not in a position yet to talk about the feedback. We're just very happy that it is out there. It is being used.

We got the mechanics right. Sometimes these are very sophisticated calculations. They have to run in the cloud. So we've gotten over a lot of the barriers, but now we're just waiting for the actual feedback. That's going to take a little bit of time because you have to make the prediction, then you have to go and test it, right? So -- but we're looking forward to, in the near term, getting that feedback, incorporating it into the technology. That's how we keep improving. That's why all these interactions that we have with so many customers has really helped us build an incredible platform, so this will be yet another example where we will take that feedback, put it -- incorporate the learnings into the software, make another release and keep doing that iteration until we have yet another sort of new breakthrough technology like we've done before. So -- but nothing concrete I can say.

Wonderful. And I guess we touched on this before as it relates to predictive tox in the FDA's evolving stance on AI and drug development and -- how is that influencing adoption of your platform?

Yes. Right. I mean, we sort of touched on this earlier. I think everybody in the industry is well aware of the FDA's road map. I think companies are taking that seriously. And I think it's really helped in engagement with companies there. We don't have to spend a lot of time explaining why it's important what we're doing, let's put it that way.

Yes. Wonderful. Maybe to go down into the weeds a little bit, but SGR-1505 and B-cell malignancies. So looking back at initial Phase I dose escalation data presented at EHA and ICML in June of this year. What are the key takeaways from the data readout? How do you envisage its role in the treatment landscape for B-cell malignancies?

Yes. So we were very excited to release that information on our first molecule to go in the clinic. The first thing that I alluded to earlier was that MALT1 is a new mechanism, the prior clinical release from a third party has demonstrated dose-limiting toxicity, and that molecule was discontinued. So the big question was, is MALT1 safe mechanism that can be given to patients in an ongoing fashion. And we're very pleased with the safety profile of the drug. No dose-limiting toxicity, no deaths on trial. So we think that we derisk that whole question of safety.

Very happily also we can report that we hit the PD target for this. So we've shown really shutting down NF-kappaB signaling and that translated into early signs of efficacy in a dose escalation trial. Now how can this be used in the treatment landscape? Very briefly, the treatment landscape in B-cell malignancy has been dominated by BTK, $11 billion franchise, also venetoclax and BCL2. There has been almost no other small molecules in the B-cell malignancy space. PI3-kinase came and then sort of went. And so MALT1 represents a brand new, in our hands, very well-tolerated mechanism for B-cell malignancies. And now the question is, how do you go ahead and follow up on that? And that's something we've been discussing with partners because we view this as a mechanism that mid-stage and beyond development is best done in partnership. And so yes, excited about having this new mechanism on the landscape for patients.

Sure. Sure. Still on 1505, How do you interpret the asymptomatic bilirubin elevation observation and how they compare to prior MALT1 inhibitors?

Yes, absolutely. So MALT1 is a relatively new mechanism, as I said, is protease. The orthosteric site, that's where it's a protease for that orthosteric site where the sort of ligands bind is pretty large. Those drugs were not very drug like. So everyone's gone after now an allosteric site. And that allosteric site for some reason does have -- the compounds have activity at what's called UGT1A1. This is an enzyme that also is responsible for clearance of bilirubin. So this class of allosteric inhibitors definitely has this UGT1A1 effect. The prior compound that I mentioned that we discontinued, essentially had grade 3 and grade 4 bilirubin elevations, including some signs and symptoms that would make it very difficult for patients to stay on.

Our drug, on the other hand, if you look at the Grade 3 level, much, much lower. While we do tickle it, particularly in people who have mutations in UGT1A1, there's a disease called Gilbert's that people walk around with. So we do see a little bit of this, but we don't believe because there's no signs or symptoms that this is problem for SGR-1505.

Sure, sure. And maybe a question to bring it back to the broader audience. So you think about the development and discovery of 1505. Why could a machine do it and a human couldn't? What's the differentiating factor there that application of your platform? Does that make sense?

Yes, completely. I'll take a crack at that. Such a great question. So drug discovery is a very complex multiparameter optimization problem. When you design a molecule that's potent, which is pretty easy to do, you just add a little bit of carbon atoms to it, you make it a little bit more hydrophobic, it will tend to bind more tightly to binding site. But then it won't be soluble. And then when you try and improve the solubility by making it a little bit more polar, now it's not permeable. And you go around in circles like this, [ whack-a-mole], right?

In the middle, while you were trying to improve solubility, you messed up the potency. Now you go back and try and fix the potency, now you have hERG or now you have a SIP or now it's completely insoluble and so on and so on and so on. It's a very, very complicated multiparameter optimization problem. And it turns out that if you just do things by brute force, which is basically just work on it for a few years, make a few thousand molecules, the chances that you find a molecule that balances all those things, where it's potent, selective, soluble, permeable and so on and so on and so on and say is extremely low. That's the statistic that you're all aware of. That's why 5% of molecules make it all the way through. It's because of that.

What does computation do? It allows us to explore literally hundreds of billions of molecules. That's what it takes to find that really unique molecule. That's the ZAR molecule that somehow is both potent, soluble, permeable, selective. I mean that's a crazy thing. It shouldn't happen if you think about what I was just saying. So it's the scale. You have to explore huge numbers of molecules accurately. In other words, you have to be able to predict affinity, selectivity, right, all that accurately. Otherwise, of course, it's nonsense.

So that's why it's the most complicated, I think, multiparameter optimization problem that we face as -- in humanity. I know that sounds kind of exaggerated, but I really think that's the case. It's really hard. And so you need that help of exploring hundreds of billions of molecules to find that magical molecule.

I guess, can you describe the reason for exploring strategic alternatives for further development of 1505. I think I know, but...

Yes. I mean I think, again, new mechanism on the landscape, there's work to be done, right? Any new mechanism enters the landscape requires deep work in the clinic. We talked about combinations. While we do -- we got Fast Track designation off the back of our dose escalation in Waldenström's, 100% response rate. But that requires a large company or a focused dedicated company to continue the development of this asset.

In our configuration as a company, we think that that's something that's best done through some sort of partnership and that's why we've elected strategic options. We believe we've done a good job with the discovery and the early development and derisking of the molecule. Now it's time to hand over...

What we're good at.

Yes. It's what we're good at. And we think it's time to hand over to someone else. Now we're not handling everything over, of course. We're going to keep an interest in the program from a financial point of view, right? We've got royalties and milestones on a lot of different programs. It just so happens -- we're doing this on a program that we did the initial Phase I.

Sure, sure. Was that the reason you thought...

More or less. So I've got -- maybe to ask this question is that I wonder just thinking more strategically about your business that what it should be really good to set up to do is to get leads to -- in the hands of those that have the resources to conduct the trials. It's not the value that you had, the value in getting the molecule right.

That's right.

In theory, you've got, I'm not downplaying it, but an appendage of the pipeline, which is really just to advertise to the industry that this is what you can do.

Yes, yes. That's right.

And that's what the strategy is going forward. And therefore, just because you have out-licensed, it doesn't indicate in any way, shape or form that there's low confidence in the program.

Exactly. 100%. I think Karen said it really well.

I mean we've actually partnered pretty much most of the ideas we've come up with. This one just happens to have gone a little bit further than the others.

Sure. Still on 1505, but can you elaborate on the rationale and timing for pursuing combination studies with BTK and BCL2 inhibitors?

Yes. So as I mentioned, in Waldenström's, we're seeing this 100% response rate. It's very exciting. That's the monotherapy opportunity. But that's a small population. If you think about where BTK inhibitors were originally approved, it was an MCL, but people went on to -- sorry CLL and other large indications. We believe that the monotherapy in Waldenström's is the real opportunity, but it's small. And the big opportunity is now combining MALT1 in combination with BTK and BCL2 across all of those indications that have already been established for BTK. So that's the reason for that combination of science experiment that needs to be done, but it leads you very much into the commercial opportunity.

Sure. Wonderful. I've got some additional pipeline questions here, but what led to the decision to discontinue 2921 in AML and MDS? And what lessons were learned from its development?

Yes, great question. Obviously, unfortunately, we did decide to terminate that program. The primary reason for that was not the molecule. The molecule was a very, very nice molecule in terms of potency, selectivity, all the things that we designed into it. This was in relapsed/refractory AML, very difficult patient population, very huge unmet need. Those patients are immunosuppressed generally.

Now we had along with academic KOLs identified that CDC7 was phenomenal at shutting down these AML cells, and that's why we went after it. The issue is that just like venetoclax, which is a huge drug, BCL2 inhibitor, this immunosuppression does leave patients susceptible to life-threatening infections. And that's what happened, obviously, during this trial.

Now when we looked at the whole thing holistically, while there was activity and the opportunity to kind of do another venetoclax here, we decided that, that was not a good fit for Schrödinger along the lines that we've just been talking about, not a good way for us to be spending our time and money.

Yes. Wonderful. Can you give us an overview for expectations of upcoming Phase I readout with 3515 in advanced solid tumors?

Yes. So for those who don't know, 3515 is a Wee1/Myt1 compound. It benefits from synthetic lethality where these 2 mechanisms working together should open up the therapeutic index. So we've been in the dose escalation trial for just over, I don't know, about a year now, I think, basically studying safety, PK, PD and signs of preliminary efficacy. And so what we're looking for there is obviously very early because you can't compare to a Phase II or III study, but very early signs that we have hit the target and that we have essentially got initial signs of antitumor activity. So that's what we're looking for in this -- just as we did with the MALT1, understand with 2921 where we were, it would be the same with 3515.

Wonderful. Just deviating a bit, but on partnerships and commercialization. So can you give an overview of your most advanced biopharma collaboration partnerships? What validation do these partnerships provide for your platform?

So the most advanced -- I mean, as Ramy has said a couple of times today, our first partnership stemmed back 20 years, and some of those compounds have actually gone all the way to the market. So we were early collaborators with Agios on IDH1, Gilead with Nimbus, obviously, the Nimbus projects, we worked on with the ACC inhibitor that's in Phase IIb, I think, with Gilead and then with Morphic, that collaboration, that's in Phase IIb in the hands of Lilly. So there's quite a few late-stage compounds that I think either because they were acquired or they kept moving or that they were approved to validate that we can make molecules with this platform that enter development, stay in development and really helping patients.

Wonderful.

Yes. And Sean, these programs all have commercial milestones and royalties associated with them. We don't disclose those. We don't guide to that, but in our drug discovery revenue, what you're seeing today is a growing portfolio of collaborations. In the last few months, we've extended our collaborations with Ajax and with Lilly and with Otsuka. That's what's contributing to the growing drug discovery line. But in the future, over the years to come, there's another kind of layer of stream from milestones and royalties that we expect.

Sure. And how much visibility do you have on milestones and royalties, given it's a third party?

Well, I'd say during the active collaborations, there are milestones, obviously, during the discovery phase, we've got a lot to do with that because we're helping to steer those programs. Once things enter the clinic, our work is done, we do have ongoing joint research committees where we meet once or twice a year to understand how those programs are going. But less visibility, clearly, and there's also portfolio and pipeline strategy at these companies that we have nothing to do with.

Okay. Thinking about 1505, what would an ideal partner look like?

I mean I think the most important thing in the very near term is a focus on the mid-stage development, getting those combination studies and potential registration studies designed and executed. But really, I think a commercial powerhouse that has already established a franchise in B-cell malignancies would be ideal, right, to be able to leverage that ultimately, I think, will be the way to maximize the potential of MALT1.

Sure. And I guess with broad of these development programs, like where does the sovereignty of the data set? Who owns it? Can you actually generate a data set from a partnered program, but then you can leverage that data to...

That's a really important question. There's 2 ways to think about data. The first is the IP that then goes on, obviously, with the compound all the way to the market. That IP is exclusive to our partners. And at the moment, that we partner the program, we hand over the IP and all the data that's related to it. But there's another kind of data...

Yes and I can address that, yes. So I can tell you every agreement we have, every one, any improvement to the technology or the platform we own. That's the answer to that question. And no matter what form it comes in, we own that outright.

And then there's a very strict firewall that separates off collaboration data from our platform data.

When we say there are synergies between the drug discovery and the software, that's one of them, that we own -- we bet all that know-how, all the improvements to the platform, everything we learn, that gets all incorporated in the platform and the whole industry benefits from that, including us, by the way, from our own programs.

Wonderful. We touched on Ajax collaboration. Can you share more about the -- more detail about the expanded collaboration and its potential impact on milestones and revenue?

Richie?

Yes, I'm happy to cover it. We have -- we expanded the collaboration a few months ago to add another JAK target in the I&I space. In terms of the economics on that, it mirrors the original agreement, but we've expanded it to include commercial milestones as well as royalties. I would not expect those to contribute meaningfully in the near term, but it creates another long-term opportunity for us.

Wonderful. I might start asking a little bit about the financials and the longer-term outlook. But how are you managing operating expenses with scale and clinical development? And what impact does the recent restructuring that I think was in May had on your financials?

Yes, I'll start there. I think I wouldn't say that we're scaling clinical development. We've spent a lot of time talking about 1505 and seeking a partner there or seeking out strategic partners. Our 3515 program is early in the Phase I stage. But we aren't guiding and we're not talking about adding additional programs into the clinic. So just to address the clinical spend piece, from an overall company expense profile, we announced in May a reduction of $30 million of operating expenses, most of that will be realized this year. Some of that will still balance out in the first half of next year. But overall, if you look at our results for Q2, we have a great profile, which is growing revenue, mid- to high teens, but also year-on-year expense decreases, mostly driven by the R&D line item.

Sure. And the software revenue growth, I think it's 10% to 15% for...

That's what you guided to this.

Just sort of the drivers behind that. And I guess sort of closing the adoption gaps amongst the mid-tier customers.

Yes. I can.

Go ahead and start.

Yes. The primary source of growth at the moment, to the extent that biotech companies aren't doing so well right now. It's pharma companies, our existing customers. So every pharma company is using our software. But there's a pretty big range and how much they're using it. We have a handful that are using it at a very significant scale, and then another handful that are quite a bit lower scale. So a huge opportunity and we think it's inevitable. Of course, every pharma company is going to be using it at same scale, but it's a process. Some pharma companies are a little bit slower than others.

So growing those large pharma companies that are sort of utilizing the software at a smaller scale is an opportunity. It also is an opportunity -- even the customers, this is important. Even our largest customers are actually still underutilizing the software relative to what we're doing internally. So that even in and of itself is an opportunity as well. But that's the -- that's a big part of the focus.

Sure. And maybe help me model question. But how do you measure success in your software business beyond revenue? What metrics best reflect customer engagement and platform adoption?

Yes. ACV is a metric that we focus on for customers greater than $5 million in ACV. In 2023, we had 4. In 2024, we had 8. So that's grown substantially. Where we keep future growth is the divide between customers greater than $5 million and customers greater than $1 million, 8 customers greater than $5 million, about 30 customers greater than $1 million. So that's where we seek the opportunities to step up relationships, deploy our technology at scale and increase the throughput of the access that they have.

I think the retention rate for customers that are spending over $500,000, over $0.5 million, is essentially 100. I mean that's a pretty important metric. That says a lot about the efficacy of the technology. I don't think you are renewing the software over and over again, right, if it isn't actually working. Another thing I looked at recently, I don't know if this is interesting enough, but I'll just throw it out there anyway is we looked recently at the number of patents that pharma companies and biotech companies submit where there's mention of the use of our software, like over 2,000 or something in recent times.

I think that's another metric. So it's not exactly a financial. It doesn't show up in the SEC fund. But I'm trying to give you a sense, right, to answer your question about just indicators, right? As you said, metrics that demonstrate something about the efficacy of the platform, the potential for it to grow, the impact it's having that are just beyond the sort of revenue.

Sure. you. And your long-term vision for balancing profitability with investment in the platform and innovation. How do you think about that?

Yes. Well, profitability is a goal. Obviously, we're not guiding to exactly when, but I think it's a strong statement that it's a goal. And so -- but to do it in a way that allows us to continue to innovate the platform. We are the leaders in this space. We are innovating. We define the field. All the breakthroughs here about in this space are -- happen inside Schrödinger, and then other companies come and try and replicate it to not very successfully because of the effort that we're putting into it.

So we think that's really important. Where our goal is to achieve profitability, continue to grow the software business, but do it in a way that allows us to continue to innovate in the platform. It's such an important part of our business. And then I think you heard how we're helping the situation by our plans around partnering the clinical programs at the point where they become very expensive, which is obviously the mid-stage and later-stage clinical programs. So that's -- well, that's pretty good timing.

Perfect. But is there any message you would like to leave the audience with before we close today. Any message?

We haven't already covered. I think this conversation has been quite good at covering everything. So yes, no, I don't think so. Great job.

Fantastic. Well, thank you to 3 of you for participating. Wonderful to host you.