Good morning, everyone, and welcome to Morgan Stanley's Global Healthcare Conference. I'm Sean Lammon, Head of US Midcap Biotech Equity Research here at the firm. Before we commence, for important disclosures, please see Morgan Stanley Research's disclosure website at www.morganstanley.com/researchdisclosures. 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. Welcome to the three of you. Thanks for having us. You're welcome. Maybe just to commence, we have some macro questions that we're asking all of our companies. The first one is, with China's rise in biotech innovation, how are you thinking about Schrödinger's competitive position, and will this influence your R&D and business development strategy going forward? Yeah, 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 a severe limitation of sort of machine learning-based methods, you know, methods that are solely based on machine learning. To the extent that our platform is built on first principles and on physics-based methods, it allows the discovery of highly differentiated molecules that can solve challenging design problems. 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. That's, I think, do you have anything to add, Richie? I just came back from China. We wanted to sort of give a landscape for ourselves. I think that there's opportunities. First of all, people view Schrödinger's platform as the gold standard. That's what we kept hearing. As those companies evolve to working on more novel targets and globalize, I think having relationships with those companies is something that is important for us to do just with our whole customer base. Absolutely. Yeah, wonderful. Thank you. 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. 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 a model effective. A small training set or a training set that's not representative of the problem, machine learning models aren't very effective. A large and well-trained set that represents the problem well and thoroughly, AI is very powerful. We see many examples of that. That's the case also in chemistry and design of molecules. 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. 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. For chemistry, there's not a lot of data. We've developed methods using first-principles physics that can produce experimental data, essentially the equivalent of experimental data, but on a scale that's many, many orders of magnitude faster and cheaper than experiment. Here's what we're doing. Here's how we're leveraging AI. We are building massive training sets using first-principles physics. That is essentially amplifying machine learning, right? Because, again, you need a training set. We can generate, actually, in one day, the equivalent of about 10 years' worth of experimental data. I mean, that's extraordinary at, obviously, a fraction of the cost. 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. There's another area that's 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. There's a shortage of people that actually can, you know, that are experts that can be able to run these. There's no shortage of chemists and biologists on research teams. We're also developing, this is 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, super exciting. Yeah, very. Yeah. Question on the macro side before I'll get Schrödinger specific. What's been the most impactful on your company from the regulatory side, if anything? Is it FDA, MSN, tariffs? Yeah, 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? By designing safer molecules, right? You don't have to test as many, do as many tests, and have as many failures. You put safer molecules into animals. We're really excited about our, what we call, predictive tox initiative. It's funded by the Gates Foundation with a rather generous grant. We've made great progress. We actually released the beta recently. That announcement of the roadmap from the FDA has really been impactful in generating a lot of interest in computation. That obviously benefits us tremendously. I don't know if. Yeah, I mean, I think in the near term, that's one of the most specific things. The FDA is clearly influencing AI and computation across the whole gamut, from predicting safety, but also in how they operate as an agency with respect to drug development. Those are welcome changes. Beyond that, I think a lot of these other topics don't necessarily impact Schrödinger directly. Yeah, that's right. Thank you. We started last week, was it this week? Last week, publishing a regular AI publication as our first one. We've called it Looking for My Mom, which is a maximally optimized molecule. Perfect. How does your physics-based platform combine with AI, machine learning, accelerate drug discovery? You know, what makes it scalable across programs? Yeah, I touched on that, but let me elaborate a little bit more. Again, as I said, machine learning requires accurate training sets. We can use the physics-based methods to generate accurate data sets that are on a scale that actually makes machine learning interesting. That is 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. That is tied directly to the number of molecules that you can explore computationally. 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. That's how I think that's the answer to that. Maybe just one other thought here is, I think we're looking forward to how is computation and AI-related approaches impacting the 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 PIC2. There are a lot of proof points about the impact of computation to look back on. Absolutely. As well as to look forward to. Absolutely. Thank you. What are the most compelling examples of platform validation from your proprietary and collaborative partners? Yeah, I think Karen just. I just started talking about, obviously, the two big acquisitions of molecules. Actually, there are 15 molecules that have entered the clinics. I mean, not disclosed, obviously, they're from our collaborations with Big Pharma. We ourselves this year published the second MORT1 inhibitor to go into the clinic, showing that we have a very differentiated profile that was optimized using our platform. It took us 10 months to find that compound, and we only synthesized 80 molecules. Those stories of the work we've done with our equity partners, Nimbus, Morphic, Structure, a 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's not known, we can't tell anyone what the target was. That is, I think, another example of a company putting up $150 million because they thought we could help them win. Sure. Wonderful. Thank you. Maybe just to go back and double down a little bit on predictive tox. What feedback have you received from beta testers so far? Yeah. We just released the beta, and we just started to have customers starting to use it, beta testers, our partners, you know, close partners. We obviously work with companies that we're very, very close to, where we can rely on their feedback, and they won't punish us too much if there are little issues here and there, which, of course, there always are with betas, mostly technical things, just mechanics of running it. 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 that have to run in the cloud. We've gone over a lot of the barriers. 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? 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 have really helped us build an incredible platform. This will be yet another example where we will take that feedback, put it in, 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. Nothing concrete I can say right now. Wonderful. Thank you. I guess we touched on this before as it relates to predictive tox, but in the FDA's evolving stance on AI and drug development, how's that influencing adoption of your platform? Yeah, right. I mean, we sort of touched on this earlier. I think everybody in the industry is well aware of the FDA's roadmap. I think companies are taking that seriously. 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. Yeah, wonderful. Yeah, yeah. Maybe to go down into the weeds a little bit, but SGR-1505 and B-cell malignancy. Looking back at the initial phase one dose escalation data presented at EHR 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? Yeah, we were very excited to release that information on our first molecule scan clinic. The first thing that I alluded to earlier was that MORT1 is a new mechanism. The prior clinical release from a third party had demonstrated dose-limiting toxicity, and that molecule was discontinued. The big question was, is MORT1 a safe mechanism that can be given to patients in an ongoing fashion? We're very pleased with the safety profile of the drug. No dose-limiting toxicity, no deaths on trial. We think that we de-risked that whole question of safety. Very happily, also, we can report that we hit the PD target for this. We've shown really shutting down NF-kappa B 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 have been almost no other small molecules in the B-cell malignancy space. PI3 kinase came and then sort of went. MORT1 represents a brand new, in our hands, very well-tolerated mechanism for B-cell malignancies. Now the question is, how do you go ahead and follow up on that? 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. Yeah, excited about having this new mechanism on the landscape for patients. Sure. Still on SGR-1505, how do you interpret the asymptomatic bilirubin elevation observation? How do they compare to prior MORT1 inhibitors? Yeah, absolutely. MORT1 is a relatively new mechanism. As I said, it's a protease. The orthosteric site, that's where it's a protease for that orthosteric site where the sort of ligand bind is pretty large. Those drugs were not very drug-like. Everyone's gone after now an allosteric site. The allosteric site, for some reason, does have activity. The compounds have activity at what's called UGT1A1. This is an enzyme that also is responsible for the occurrence of bilirubin. This class of allosteric inhibitors definitely has this UGT1A1 effect. The prior compounds that I mentioned that was 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 levels, much, much lower. What we do to fill it, particularly in people who have mutations in the UGT1A1, there's a disease called Gilbert that people walk around with. We do see a little bit of this, but we don't believe, because there's no signs and symptoms, that this is a problem for SGR-1505. Thank you. Maybe a question to bring it back to the broader audience. You think about the development and discovery of SGR-1505. Why could a machine do it and a human couldn't? What's the differentiating factor there, the application of your platform? Does that make sense? Yeah, oh, completely. I'll take a crack at that. Such a great question. Drug discovery is a very complex multi-parameter 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'll tend to bind more tightly to binding site. Then it won't be soluble. When you try and improve the solubility by making it a little bit more polar, now it's not permeable. 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 herd, or now you have a sip, or now it's, you know, completely insoluble, and so on, and so on, and so on. It's a very, very complicated multi-parameter optimization problem. 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 safe, is extremely low. That's the statistic that you're all aware of. That's why 5% of molecules, you know, 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 bizarre 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. 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, all that accurately. Otherwise, of course, it's nonsense. That's why. It's the most complicated, I think, multi-parameter optimization problem that we face as a, you know, in humanity. You know, I know that sounds kind of, you know, exaggerated, but I really think that's the case. It's really hard. You need that help of exploring hundreds of billions of molecules to find that magical molecule. Thank you. Can you describe the reasoning for exploring strategic alternatives for the development of SGR-1505? I think I know, but yeah. Yeah, I mean, I think, again, new mechanism on the landscape. There's work to be done, right? Any new mechanism that enters the landscape requires deep work in the clinic. We talked about combinations. We got fast-tracked over the nation off the back of our dose escalation and well-dosed drugs, 100% response rate. 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. That's why we've elected strategic options. We believe we've done a good job with the discovery and the early development and de-risking of the molecule. Now it's time to hand over. That's what we're good at, right? Yeah, it's what we're good at. We think it's time to hand over to someone else. We're not handing 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 one with. I might go, is that the reason you thought? More or less, yeah. 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 in the hands of those that have the resources to conduct the trials. That's not the value that you had. The value is that bit in getting the molecule right. That's right. In theory, you've got, I'm not downplaying it, but an appendage of a pipeline, which is really just to advertise to the industry that this is what you can do with the platform. Yeah, that's right. That is what the strategy is going forward. Therefore, just because you've had a license, 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. 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. Yeah. But yeah. Sure. Yeah, good. We're still on 1505. Can you elaborate on the rest on timing for pursuing combination studies with BTK and SGL2 inhibitors? Yeah, as I mentioned, in well-dosed drugs, we're seeing this 100% response rate, which is very exciting, that's a monotherapy opportunity. That's a small population. If you think about where BTK inhibitors were originally approved, it was in MCL, but people went on to be able to, sorry, CLL and other large indications. We believe that the monotherapy in well-dosed drugs is a real opportunity, but it's small. The big opportunity is now combining MORT1 in combination with BTK and BCL2 across all of those indications that have already been established for BTK. That's the reason for that combination science experiment that needs to be done. It leads you very much into the commercial opportunity. Sure. Wonderful. Thank you. I've got some additional pipeline questions here. What led to the decision to discontinue SGR-2921 in AML and MDS? What lessons were learned from its development? Yeah, 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. We had, along with academic KOLs, identified that CDC7 was phenomenal at shutting down these AML cells. That is 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. That is what happened, obviously, during this trial. 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. Yeah, yeah, wonderful. Can you give us an overview for expectations of upcoming phase one readout with SGR-3515 in advanced solid tumors? Yeah, so for those that don't know, SGR-3515 is a V1-MYT1 compound. It benefits from synthetic lethality, where these two mechanisms working together should open up the therapeutic index. We've been in a dose escalation trial for just over, I don't know, about a year now, basically studying safety, PK, PD, and signs of preliminary efficacy. What we're looking for there is obviously very early because you can't compare to a phase two or three study, but very early signs that we have hit the target and that we have essentially got initial signs of anti-tumor activity. That's what we're looking for in this, just as we did with the MORT1 inhibitor, understand with SGR-2921 where we were, it'll be the same with SGR-3515. Wonderful. Thank you. Just deviating a bit, but you know, on partnerships and commercialization. Can you give an overview of your most advanced biopharma collaboration partnerships? What validation do these partnerships provide for your platform? The most advanced, I mean, as Ramy has said a couple of times today, our first partnerships going back 20 years. Some of those compounds have actually gone all the way to the market. We were early collaborators with Agios on the IDH1. Gilead was Nimbus projects. We worked on with BCL2 inhibitor. That's in phase 2B, I think, with Gilead. Then with Morphic, that collaboration, that's in phase 2B in the hands of Lilly. 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, validate that we can make molecules with this platform that enter development, stay in development, and are really helping patients. Oh, wonderful. Yeah, and Sean, these programs all have commercial milestones and royalties associated with them. We don't disclose those. We don't tie to that. 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 Eli Lilly and with Otsuka. That's what's contributing to the growing drug discovery line. In the future, over the years to come, there's another kind of layer of stream from milestones and royalties that we expect. Sure. Thank you. How much visibility do you have on milestones and royalties given it's a third party? 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. There is less visibility, clearly. There is 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 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. 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 MORT1. Sure. I guess with a lot of these development programs, where does the sovereignty of the dataset, like SARA dataset, who owns it? Can you actually generate a dataset from a partner program, but then you can leverage that data too? Oh, that's a really important question. There's two 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. At the moment that we partner the program, we hand over the IP and all the data that's related to it. There's another kind of data that. I can address that. I can tell you every agreement we have, every one, any improvement to the technology, to the platform, we own, period. Okay. That's the answer to that question. No matter what form it comes in, we own that outright. There is a very strict firewall that separates off collaboration data from our platform data. Yes. Yeah. Sure. When we say there's synergy between the drug discovery and the software, that's one of them, you know, that we own. We've bent all that know-how, all the improvements to the platform, everything we learn, that gets all incorporated in the platform. The whole industry benefits from that, including us, by the way, from our own, you know, our own programs. Yeah. Wonderful. Yeah. We touched on the Ajax collaboration. Ajax, yeah. Can you share more detail about the expanded collaboration and its potential impact on milestones and revenue? Yeah. Richie? I'm happy to cooperate. We expanded the collaboration a few months ago to add another Ajax target in the INI 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. How are you managing operating expenses with scaling clinical development? What impact has the recent restructuring, I think it was in May, had on your financials? Yeah, 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 SGR-1505 and seeking a partner there or seeking out strategic partners. Our SGR-3515 program is early in the phase one stage, but we aren't guiding and we're not talking about adding additional programs into the clinic. Just to address the clinical spend piece, from an overall company expense profile, we announced in May a reduction in $30 million of operating expenses. Most of that will be realized this year, if some of that will still balance out in the first half of next year. 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. Thank you. The software revenue, I think it's 10% to 15% for. That's what we got into this year. That's sort of the driver behind that and closing the adoption gaps amongst some of your key customers. I can go ahead and start that. You know, the primary source of growth at the moment, to the extent that biotech companies aren't doing so well right now, is pharma companies, our existing customers. Every pharma company is using our software, but there's a pretty big range in 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. A huge opportunity, and we think it's inevitable, of course, every pharma company is going to be using it at the same scale, but it's a process. Some pharma companies are a little bit slower than others. 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. That even in and of itself is an opportunity as well. That's the focus. That's a big part of the focus. Sure. Thank you. Maybe a help-me-model question. How do you measure success in your software business beyond revenue? What metrics best reflect customer engagement and platform adoption? Yeah, ACV is a metric that we focus on. For customers greater than $5 million in ACV in 2023, we had four. In 2024, we had eight. That's grown substantially. Where we seek future growth is the divide between customers greater than $5 million and customers greater than $1 million. Eight customers greater than $5 million, about 30 customers greater than $1 million. 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. Wonderful. I think the retention rate for customers that are spending over $500,000, over half a 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're using 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 or not, 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. It's like over 2,000 or something in recent times. I think that's another metric. It's not exactly a financial metric. It doesn't show up in the SEC fund. 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 numbers. Sure. Got you. Thank you. Your long-term vision for balancing profitability with investment in the platform and innovation, how do you think about that? Profitability is a goal. Obviously, we're not guiding to exactly when, but I think it's a strong statement to say that it's a goal. 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 that you hear about in this space are happening inside Schrödinger. Other companies come and try and replicate it, not very successfully because of the effort that we're putting into it. We think that's really important. 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. I think you heard how we're helping the situation by our plans around partnering the clinical program at the point where they become very expensive, which is obviously the mid-stage and later stage clinical program. That's pretty good timing. Perfect. Is there any message you would like to leave the audience with before we close today? Any what? Any message? That we haven't already covered? I think this conversation's been quite good at covering everything. Yeah, no, I don't think so. Great job. Fantastic though. All right. Thank you to the three of you for participating. Thank you. It's wonderful to host you. Yeah, it was great to speak.