Good to go? Hot mic. Lovely. Welcome everyone back to our afternoon session of the 2026 Leerink Partners Global Healthcare Conference. For the audience, I'm Mani Foroohar, Senior Analyst at Leerink Partners. I'm hosting for this session the team from Schrödinger. Ramy, Richie, how are we doing? Great to be here. Thank you for having us. Awesome. Welcome to my adopted hometown. Yeah, right. 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 New York. We just had to pull you guys down, unfortunately. Yeah. The delightfulness of Miami aside, let's talk about the evolution towards ACV. I'm just gonna dive right in. Sure. Dive right in. I'm assuming people here know the general contour of the primary business. Let's dive into the transition to ACV. To what extent that is a change operationally? To what extent that is a change in how existing contractual relationships are accounted for or recorded. I think there's a little confusion a mong investors on, well, how real of a change is this? How much of just accounting for economic activity that would have happened anyways? Yes. I'm sure we can clear it up. Richie, you wanna- Yeah. 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. It actually helps us support and deploy to our customers in a more accelerated way. This is. It's just a transition within our existing framework. It does have the impact of declining revenue this year. Just to spend a minute on that 'cause it is a 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%-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. Because most of our deals are in Q4, just given our customer budgetary cycles, the deals that we book 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. Because of that, those two features, 2026, we expect revenue to decline and ACV to grow. Over the course of a three-year time period, we think that will start to even out as we transition over most of the book to hosted. Let me channel some of the investors that are a little less familiar with these dynamics. Okay. They would say, for a rapidly growing business, your booking revenue upfront is most appealing because you have lots and lots of new ACVs. 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 new contract ACVs? Are they all just plotting against us? 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. Well, they don't tell you. First time I've heard that. What's that? They don't tell you. Yeah. No, they tell me. 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. It's actually kinda the opposite. Does that clear it up, do you think? That does clear it up. Good. There's nothing further I can add there. That was very clear. If ACV is not growing, the business is not growing. Yeah. If ACV is growing, the business is actually growing. There's no trickery. I hate using that word at all in the sentence, but I felt it just seemed like we were forced to use it. I mean. It's good to have the opportunity to actually say. You were forced by me. I agree. Yeah. Cool. Okay. Diving into another dynamic that I think investors find challenging is thinking about how to model and predict s omebody's larger, chunkier, you know, almost biotech partnership like f und flows. Yes. Which are a little different than the base sort of software-like business. Right. Right. How should investors track the health of that market, the demand of those partnerships? Yeah Sort of the quote-unquote "pricing power," i.e., the terms you can demand in those deals? Yeah. Well, I think the first thing to say is we have an unbelievable track record with these collaborations. Really, I think it's underappreciated. Probably, it's our fault. We haven't talked enough about it. We've done a very large number of collaborations, starting from co-founding companies like Nimbus and Morphic and Structure 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. What did we report? $650 million in cash from equity stakes, from milestones, from upfronts in the last five 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. This is a real business. This is not an N of one. It's not an N of two. I mean, you can't count. It's 16 actually, right? What I just said as far as just that, and it's many, many more collaborations. 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, you know, it stands on its own. 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 this, 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. All that success and validation has resulted in a change in the industry and how computation is deployed. Obviously we're the main beneficiaries of that right now. I think that's what's important to keep in mind, the synergies between the businesses. They're not separated. Does that? It does. Did I answer, in any way, what you were asking? It gave useful perspective. Okay. I think one of the other questions that people have is when they think. Ab out this end market, there is broad concern around most businesses that sell software of any sort. I mean, I'm speaking about very broadly. Well, what's gonna get disrupted by AI? Yeah, yeah. Is this gonna get displaced by AI? Sure. Are four kids somewhere just gonna vibe coding? Yeah, yeah. Their own Schrödinger? Yeah. Talk to me about why that's not gonna happen. Yep. How you think about parts of the business or parts of your counterparties that are susceptible to that kind of disruption or replacement and r easons why parts of it are not. Yep. Good. There are two aspects of that. One is, can an AI model trained on experiment replace physics? That's one. 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. 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 in 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 the AI technologies or the technologies that underlie, you know, 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, 'cause you're not. You're channeling the question. Oh, this is nowhere near the worst insult I've heard. Yeah, yeah. Good. I didn't mean to insult, but. No, no. Look, it's a legitimate question. Obviously, people are asking it all over. By the way, you know, $hundreds of billions of value are disappearing because of a belief that this is. 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. You know, but I think you were hinting at this. I mean, let's talk about maybe legal software. I hope that doesn't insult anybody or anybody's family member. You know, 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. Not for a little while, it's still a number of years away, but those are gonna be the first to get replaced. 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. We understand very deeply what its capabilities are. When we tell you this is not replacing what we're doing, I hope people believe us. You talked about the value of proprietary knowledge. I wanna pivot over to proprietary data sort of data as an asset. How we can think about the value either in resulting cash flows or improvement of the platform, however you wanna say, however we should measure it, of the collected 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 gonna 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. That turns out not to be the case. You will 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 a 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. This idea of a foundational model for chemistry, for design of molecules doesn't exist. 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've talked about some of the debates around AI risk. I wanna talk about how to think about monetizing your existing position. How should we think about the rollout of predictive toxicology? Ho w that additional future service, however you, whatever phrase you wanna use, how that affects the value to the customer. How you monetize that on a per contract basis. Yep. Predictive toxicology, or the ability to predict toxicity, is one of the grand challenge problems in drug discovery. It's probably the major source of failure, maybe, you know, aside from biology risk. It's a big problem, and it's a problem that occurs very late in discovery. Right, you know, in other words, you're designing a molecule and everything's 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 five years, $30 million down the tubes. It's a big problem. 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, you know, in vitro, and you test against a whole panel of off-targets. It takes a long time to do it's expensive, and you don't do it very often. You know, now there's a computational way of doing that. Now to answer your question, sorry, a little background. 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. If our budgets before, you know, 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 done experimentally. It's a huge opportunity. Really, really big opportunity. 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. We will continue to expand and continue to improve the product and continue to generate more growth from it over the coming years. Can I answer? Yeah. I'll add maybe two 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. Yeah. 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. Yeah. Let's talk about how to think about that, the scale of that contribution. I'm gonna ask for guidance, how to think about the scale of that contribution as a new module. On what time horizon that shows up in ACV inflection, potential acceleration of growth rate, revenue, however you wanna think about it. Yeah. It is built, by the way, into our guidance. We expect to generate revenue from it. ACV, I guess I should be saying. ACV. 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, well, you know, extremely positive feedback we're getting from beta customers. As is always the case with a new product, this has happened to us before. We have a lot of experience releasing new products. We have a history of creating new markets. That's what we're doing. We're creating new markets. This is a new thing. It takes time to get customers familiar with it, with the idea of doing something new, testing it. They have to test it. They don't just buy it, you know, on our promise. They have to test it. We expect there to be a ramp up. 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 toxicology module. Yeah. Let's talk a little bit about the new module. We've talked about some of the nuances of interpretations of this pivot to ACV. Yep. Let's talk about end market growth. To what extent is your growth levered to new company creation? I know the majority of revenue is not from new company creation, or new account or new account creation. How should we think about if we see a re-acceleration of IPO market, VC market, etc.? How does that flow through to you guys in terms of end user demand, and what is that for you marginally? Yeah. Do you wanna? 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. We're not relying on that for this year, but over the course of the three-year growth forecast that we've given, we are expecting, you know, 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, for example materials, et cetera. Materials. Yep. 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? Yeah. Yeah. That's a really great question. We started that division because it turns out physics is physics, and atoms are atoms. A lot of the problems in materials 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 an airplane wing is also a polymer made up of the same, you know, types of organic elements, and we can start to try and understand the properties of those polymers, using the same technologies. We have since developed new technologies that are very specific to materials science. Batteries is a good example. The electrochemistry and that's occurring at the interface of electrolyte and electrode in a battery, it turns out there's not exactly biological, you know, relevant sort of system. We have been developing new technologies, in particular, technology around battery chemistry, which requires something called machine-learned force fields. Essentially, these are a type of force field that is somewhere between the classical force fields that we use for modeling drug discovery and quantum mechanics. It's got the accuracy of quantum mechanics and the throughput of a classical force field. 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 has the potential to allow us to design better batteries for which there's clearly fantastic demand. 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 new. I think you know that was funded by a rather generous grant from the Gates Foundation. We've invested that. That's paid off. It's just coming online. You know, we're just starting to publish some of the work. We're really optimistic about the future as we did, like we did in life sciences. You know, we've been doing so much innovation. We've changed the field. You know, these free energy methods we've developed, you know, has been transformative. We think the same thing can happen in materials, but it's earlier days. As you said earlier, is the right way to think about the material side of the business as a growth driver that primarily lives on the other side of the three-year guidance that you've given. Should we think of it as a meaningful contributor within the context of this three-year period? It's both, actually. I think we will see. We're expecting that growth in materials science business will contribute to overall growth in this three-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 this, these tools? Like, what is the- Yeah. Okay, this is a sign that we should start. Yeah. Modeling more growth from this? Yeah. 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 materials science world is behind. It's behind that. It's being used, but you can tell by the number of computational chemists in materials 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. Or why would they do that if there's no technology that's actually useful? Why in the world should they invest in computation? I think that's what has to happen. It's a chicken and egg problem, right? I mean, they're not gonna invest in that until they see the technology, but then if they don't start using the technology, they're never gonna see the impact, you know. It's a little iterative process. 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 materials science companies embracing computation in the way the pharma industry did a decade ago, that will be the sign that all of a sudden that field's about to be transformed by computer-aided design just like our, you know, drug discovery field has been. In pharma, a big part of that was people training to become scientists, you know, being early adopters in academia, exposed to an academic lab as part of their PhDs. That's right. Exposing their students to it. That was a fairly long lead time. Yes. It's one of those things that it takes forever. Takes generations, right. It happens all at once when it happens. Yep. Makes it hard to model the solution for materials. I know. Well, we are putting that effort in. We have a significant education effort. We put so much work into getting academics using the technology. Schrödinger developed online courses to give to professors to teach computation to students. Yeah, it's gonna take a generation, you know, four or five years, right, as students work their way through. You know, that investment worked so well on the life sciences side, we're convinced it'll work on the materials science side too. Okay. I think that captures a lot of where we are on that side of the business. Looking forward, past the other side of the three-year plan, again, not asking for guidance, you've given a real clarity on a path to profitability. That's right. By 2028, yep. Let's fast-forward, we're sitting here in 2028. Yep. You still 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 for this, in this kind of business are pretty attractive. Like, what is the right use of incremental capital? Yeah. 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. You know, it taking four years, five years to get to a development candidate, that's not okay. It should be taking a year. The failure rate should be zero, you know, when you get to a certain point. We will always be. I hope this is the goal, is to always be the leader we have been. We've been the leader in this space. We've been defining what it means to innovate in this space. I think we are many, many decades away from saying, "Oh, we're done. Everything's all good now. Now drug discovery is as good as it's gonna get." Don't forget, you have the whole materials science, which by the way, isn't just one field, right? Materials science is a huge number of different fields, right? From aerospace, to chip design, to battery design, and countless other things, other types of materials. That's kind of an exciting thing to be a part of, to be able to drive the field forward and you know, keep making it so that we don't have to wait for 15 years, you know, for life-saving medicines or materials that change. 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 co-founding, but we've owned a very small part of those companies. I think as we get to a point that you were just describing, there'll be an opportunity for us to own more of it. I think that will also be really great for Schrödinger, for shareholders. That's another thing that we're looking forward to. That's a little hard right now, obviously, but do you agree? Yeah, just to quickly expand on that is we spent some time on the Q4 call laying out our portfolio of milestones and royalties. By that time period, I'd expect to see some of those contributing on a recurring basis at a, you know, near 100% margin. Right. Yeah. Awesome. Yeah. On that note, we are already over time. Yeah, we're a little over. Higher than my fault. I see that, yeah. That's good. Looking forward to continuing.