UBS Global Healthcare Conference - SCHRODINGER INC

Good morning, everybody. Welcome to day two of the UBS Healthcare Conference. I'm Kevin Caliendo, UBS healthcare IT and distribution analyst, and we are very proud and happy to have with us today, management team from Schrodinger's, Richie Jane, who is the chief financial officer, and Pat Lorton, who's the chief technology officer for software. Guys, thanks so much for having us. Yeah, thank you for inviting us. I should mention, I think you are the best performing stock over the last three months of like the Russell 3000, or you're one of the two, up over 100%. So congratulations. I wish one of three of us up here was smart enough to assume coverage of the buy rating three months ago when I did that. So thank you for succeeding and making me look bad. But no, congratulations in all these sense. It's been fantastic to watch. um maybe before we get into the q a why don't you talk a little bit and introduce everybody to schrodinger's more so great yeah i'm happy to do that thank you thanks um so we are a computational platform for molecular discovery um of of high epic high applicability to life science and material science customers if you look at our business it is um weighted towards life sciences but material science is a growing part of the business we monetize the platform through licensing of the software. That's the majority of the business. We also monetize the platform by doing our own therapeutics and by doing collaborations and new codes with companies in life science. If you think about our outlook and trajectory, we are a high science, high innovation company bringing new products to our customers. We're continuously evolving the platform and expanding the domain of applicability beyond just small molecule discovery, but even moving into new products such as formulations, expanding from small molecule into biologics. And we've also been innovating on the AI side. We've rolled out an agent called Bunsen, which Pat can talk about in more detail, but it is allowing our customers and users to gain efficiencies in running the software. We've also been evolving the business model a bit. We're focusing on the platform-centric elements of the company, so licensing software, doing early-stage therapeutics. We've Also announced this year that we're moving towards more hosted contracts and focusing on ACV as a result of that. That's helpful. You mentioned AI. We should probably start there, because it seems where everybody wants to start these days. It's clearly critical. We had a CRO panel yesterday where we talked about it. You can't talk about drug discovery and drug development without talking about AI. It's clear. It's really critical to the future innovation for drug discovery. Can you talk a little bit about your overarching AI strategy, how you approach it, how you use it, how you promote it even within maybe? Why don't we start there? Yeah, sure. I'd love to talk about that. What's interesting is Schrodinger had a bit of a reputation for a couple years of being, we'll say, anti-AI, and it wasn't necessarily because we were anti- AI at all. So I can actually say we tried to build the Bunsen agent, the first version of that, I think a little over four years ago now, I think when ChatGPT 2.5 came out. So we've wanted this level of AI, but a lot of the hype around AI was being completely oversold, and we viewed it kind of as our job to stand up for the physics-first deep calculation approach, but that was using AI to make predictions, making AI to drive the actual discovery process and run the calculation engines, similar to how AI is able to code now, similar To Claude Code, how it's dominating all coding. We viewed that very early on as something that could be transformational, and we were lucky because the second that that actually became true, that the harnesses became good enough, that AI was actually good enough to drive drug discovery, we started building Bunsen, like the current product, I should say, instead of experimental. And we had early working versions of Bunsen more than a year ago now. I think we first started talking about Bunsen Q1 last year. Q1 of this year. Oh, I'm sorry. Q2 of this here, yeah. But by Q4 of last year, we already had our internal scientists proving that this thing works. And were extremely bullish on its ability to really drive discovery. We've mentioned several times that a big limiting factor we've always had is there aren't enough computational chemists, talented computational chemist in the world. We have, you know, we'll visit users and we have introduced three new products and they say, I don't have enough time to use the products I'm currently buying from you. And we think not only does this solve their ability to expand the scope of prediction and drug discovery, but it also solves their ability to run it at a much greater scale. That's why we're so bullish and excited about AI. So you're solving for the chemists? is that we're in essence we're solving for all drug discovery actually i mean you look at how powerful these engines are and you can start thinking about biologists asking questions that only chemists could ask people we're actually we are now at the place where we're comfortable with a biologist asking a question that not not even a chemist could answer a phd computational chemist was required before so not all of the we're not putting computational chemists out of business there's still a lot of stuff they have to do but but the ability to simplify some of the more straightforward tasks is really going to transform the predictive abilities in this. I'll just add on the AI framework, it's obviously it's all about the data and it's about the model. And generating data in our field is extremely cumbersome and burdensome. And there's not enough experimental data you can generate to run an AI model. You have to supplement it with simulation data. And that is what our platform does. We generate simulation data in addition to experimental data at a scale and throughput that you cannot replicate with experiment and at the same accuracy as the experiment. And so that is how customers use our platform. It's generating simulation data to run the AI model. And to bring that home, our internal GPU supercomputer can run somewhere on the order of 30,000 to 40,000 accurate binding affinity calculations a day. It's not just being used for on-target anymore, but in an entire five-year drug discovery program, they might only collect 3,000 data points. We're talking 12 drug discovery programs a day worth of data that we can generate on just our supercomputer. And most of our customers are using their own supercomputers too. So the gap between predictive data and real data is already, with current technology, orders of magnitude, several orders, and it's only going to get bigger as CPUs get faster. I want to take a little step ahead. You're providing this 30,000 to 40,000 data points. What does that mean for the pharma manufacturer who's like, what does this mean for Bristol-Myers or for Amgen or somebody? Does that mean they have more targets, or does this means they have fewer targets that are much more precise. Let me clarify real quick. That 30,000 to 40,000, that's for our internal programs only. That's for internal therapeutic. This is not for your customers. Yeah. Our customers have their own supercomputers generating their own bolus. But what it means for them is every week, Bristol-Myers on every program, I mean, generic pharma on every programme is probably picking 50 to 100 molecules to synthesize. And each one of those cost a lot of money and they take weeks to turn around and the data to actually confirm that what you're designing is useful is very slow and cumbersome. If when they pick those 50, they have looked at 50,000 and they are convinced that these set of properties, they're good. The success probability goes up dramatically and success is speed, right? So when everybody is racing to get the first in class a little faster, speed is really what matters. And this allows them to operate much, much faster. That's a lot. I guess this is somewhat of a basic question, and maybe it's just my lack of understanding. What is Schrodinger's doing that others can't? Like, why can't someone recreate what you're doing on their own? What's proprietary, I mean, without giving away proprietary secrets? I'm happy to go. Well, let me start and then let me cover it from the business perspective and then cover from the details. What's one of the advantages we have is not only are we building a platform for molecular discovery, but we have an internal team of 200 people in our therapeutics business who are using that platform and giving us feedback on it. So we're not just building it and hoping it's going to work. We have a team of people who are using it and making sure that it works so that when we bring it out to a customer, it is applicable to what they're doing. You just covered most of what I was going to say, but it's great. But what I'd say, what we have been doing is, you know, we have a team of a couple hundred PhD computational chemists who are writing this code every single day. This is not the type of code that cloud code is dramatically accelerating, to be honest with you. We have a lot of parts of our code where cloud code ist helping. But this stuff, there's not a ton of published work around it. It's in the nitty gritty details. You actually need live feedback of assay data, which is what we get from our therapeutics team. and we've just been doing this, you know, for 30 years. You know, we've put over a billion dollars in our platform and we think we've actually done it quite efficiently. So it's not like someone's going to be able to shortcut that. And by focusing on squeezing out errors and inaccuracies, this is actually one place where AI really helps us. So, you Know, let's say our product's 90% accurate and someone trying to come up is not putting any of the effort in and they're 70% accurate. You might be able To justify 70 versus 90 to save a lot of money. If all you're doing is one data point, but when you're giving that tool to an AI and it's running for two weeks on its own and those errors are compounding, you need the most accurate tool. And that's kind of what differentiates us. We've built through a lot of, you know, blood, sweat, and tears, the most accurate tool to Winston had some early success, especially Bristol Myers is one that we know about. Can you explain in greater detail what this product's doing with Bristol, how it transforms your own workflows and your customer workflows a little bit? I mean, we talked about it a little bit, but what is Bristol doing with this that you know or that you can share? I mean to be honest, because of NDAs, I can't share a lot. But I can speak generically about what a customer like Bristol would do that we expect almost all customers to do. Hypothetical. Hypothetically Bristol-like company, yeah. So what it actually allows them to do is we joke that every Bunsen session, which honestly does not look that much different than a chat GPT session, aside from a lot of chemical built-in knowledge and visualizations, every Bunsen session is a postdoc. So your computational chemist went from being a single person who is busy all day long to being able to have a team of postdocs running experiments for them. And that actually allows them to push more and more data into, it's what I was talking about earlier, it empowers them to start running more calculations, more risky calculations that would have been too time consuming to take the risk and to validate that our software and calculations in general are working more out of scale. And then push that into LiveZyne, our enterprise informatics platform, which they've also publicly talked about having. So that as you run all these experiments, the real data coming in is going into Bunsen. Our experimental data is being generated and being pushed into LiveXyne. And the chemists have more information than they've ever had before to make the decision about what molecule is the right molecule. So it should be for a company like Bristol-Myers Squibb, the more they roll it out and embrace it, the more of an accelerant it will be for their discovery. And as that relates to our business model around Bunsen, as the customers are using the platform more and they have higher throughput, they'll need more licenses to run the platform. So that's how we intend to capture the value of Bunsen is through our throughput licensing. Literally, you're answering my next question, which is how do you monitor its licenses? Is it like on a per or how should we think about it? Yeah, I'll cover it. We don't charge per seat. It's per license. And the more licenses you have, the more calculations you can run in parallel. So it's quite simply, you know, you don't need, as Pat just said, we don't needs more users. We need more instances of the software running. And so it's a standard throughput-based license. Right, so no. Okay. Yeah. How can Bunsen be used outside of BioPharma? I know you talked about this. We talked about this a little bit in the idea that there might even be a greater unmet need outside of this platform. Maybe talk about that. I know this is a very big picture thing, but it was fascinating to hear. Yeah. I mean, my favorite joke, which I've been telling for a few years now, is that if you're on an airplane drinking a caffeinated beverage and reading something on your phone, Every part of that involves a chemical discovered by Schrodinger's platform, or I shouldn't say necessarily discovered, but influenced by Schrodinger's platform. We've been selling across many verticals in the material space for a long time. That said, materials businesses are not the same as life science, or especially small molecule discovery programs, where it's like you have to find a new billion-dollar asset every single year. So for materials, it's more iterative improvement than it is new billion dollar, well, hopefully more than billion-dollar assets every single years. but what we the way we think bunsen can really transform here is that where computational modeling was in life science maybe in the early 90s admittedly before my time but i've heard about it uh in terms of you know there's one or two people fooling around with it in big pharma but it's not transformative in any way and they don't really it hasn't changed it hasn'T digitally transformed how things work that's how it actually works in most materials right now and what we're What we really think the opportunity for Bunsen is, is right now where it might just be an individual off on their own, a single individual trying to make impact, if it can make that individual much more successful and steer them towards best practices. You know, a lot of our materials contracts actually have to start out sometimes for years as service contracts as we almost teach these employees how to use our software well. And we think Bunsen can actually speed up that whole adoption process so it goes to SaaS software sales faster than service. so when we talked after earnings last week you had mentioned and you kind of highlighted this earlier today too a little bit that one of the growth hurdles is the number of computational chemists or in general people that know how to actually use this program it's just a matter of like training people to use it do they come and spend time with you for you know spend two days to try to figure out when with your team like how does that work and how big of a hurdle is is it truly, to get people. It's overwhelming to hear you talk about it, never mind trying to actually run it. Your passion about it is clear. How do you get somebody to actually be able to figure this out? I'd say, why don't we tag team this one, but we have a lot of training programs and training courses available to academic institutions and non-commercial customers to have our platform be the standard as they learn about computational chemistry. But there's also a domain of applicability, which we talked about a little bit earlier. But today, our platform is used mostly by computational chemists, especially those that have advanced training in the platform. Bunsen will make that adoption curve easier. But really for us, and this is not a today thing, but I think this is the next leg for us is getting to the medicinal chemists who are, you know, working hand-in-hand with the comp chems, but enabling them to be able to use our tools so that we are not just, not only are we growing with a smaller user base, but we're actually growing with the bigger user base is kind of where we want to be as we keep evolving Bunsen. And generally speaking, just to get through numbers out there, there are about two orders of magnitudes more medicinal chemist and computational chemist. So you'll have, you Know, a fraction of a computational chemists on a drug discovery program. They're being shared across several, and then you'll have 10 to 20 medicinal chemists on that same drug discovery program. So reaching them is a much bigger, and to be blunt, medicinal chemists, because there are so many and they have such a long history of success, they run the chemistry departments with a few small exceptions. So, reaching medicinal chemist is very important, but Richie's absolutely right. We've invested a lot in training programs or education, things like that to bring people up to speed, but Bunsen really can be transformative. It's similar, we see the exact same impact in our software developer organizations where a junior developer comes in now and can actually start, you know, with the right mentorship, can start doing projects that only a fifth year could do before. I mean, the impact of cloud code is tremendous in terms of the ability to accelerate people. And we expect the, I mean we're already seeing internally, the exact thing to be happening with computational chemists where a junior person comes in and you might have only trusted them with this tiny task, but with Bunsen guiding them how to do it correctly, you can start introducing a much more sophisticated task much quicker. And we also have insights from our therapeutics team, which would allow us to have confidence that not only the comp chems, but also the medicinal chemists can use our platform and use Bonson. Computational chemists, medicinal chemist, decision makers tend to be either in the C-suite or in the running the R&D programs. How do they understand what this is? Is the pitch to them or is the pitch to the chemists first? Like with Bristol-Myers, was it the C-suite who bought into this or was it the R&D team or was there a chemist who used this and said, guys, we need to have this? I would say it's all of those categories plus AI and digital transformation officers that are our customers today. You need all of Those working together. You need the support of the actual users, but you need the R & D visionaries and the tech visionaries. who understand the power of our platform. Yeah, someone at NVIDIA actually said to me two years ago that every CEO is getting two questions from their investors right now, which is every CEO in biopharma, which is, what's your pipeline look like and what's their AI strategy? So there is definitely push from the top down to be forward-thinking in terms of AI. And we were really advantaged because our stance that AI was a little overhyped was pretty much completely agreed on inside industry. All of our users felt that in their hearts. So we are in this position of not being viewed as an overhyped AI company. And now when the push comes for AI and we finally have an AI solution, they trust us. We're not an over-hyping company. So we have both the incentives from the top and the people from the bottom saying, okay, let's do this. And I think that's actually, you know, I've talked about how overwhelmingly positive the interest is as we show this to customers right now. And I feel like that's what's driving a lot of the positivity. I think the NVIDIA C-suite is getting other questions today, too, just as some other one. While Schrodinger is less focused on pursuing its own internal programs through late-stage development, the company is very active in partnering. You've obviously, Bristol, that we've talked about. Can you maybe, from a financial perspective, talk about the economics and how to think about it's hard to model from the outside world? maybe talk a little bit about the economics, the revenue model of these engagements? On the partnerships? Yeah, we're still very focused on using our platform to discover unique and proprietary therapeutics in the earlier stages of discovery and then having the optionality to advance those programs either in partnership or through NUCOs. We just announced a few months ago the formation of Tektora, which is a prime example of that. We contributed two programs into the company and raised $55 million and retained an equity stake in the company. And in addition, I think what's really important is we not only formed the company, but Tektura is working with Schrodinger to continue advancing these molecules towards the clinic. So from a P&L perspective, you know, the investment we made into the programs is R&D. It's now transformed into an equity stake, and we will recognize revenue from Tektora for the service that we provide to the company on the programs that we're working on. Should we start to think about your equity investments as being material to the value of the company in whole? I would say not just equity investments, but also milestones and royalties. So the way we go to market is, especially on the therapeutic side, is we do very intensive multiple-year collaborations. where we not only earn revenue, but are generating IP alongside the customer. And we earn milestones and royalties as the program progresses down the clinic and into commercialization. So our equity portfolio has delivered substantial returns to the company since the IPO. I think that number is totaling close to 500 million now in distributions. So, yeah, we are very proud of the work we've done in collaboration with other companies and the benefit of working from a computational-first approach. I have a question from the audience, and I want to get it in because it was more relevant. I missed it earlier. When and what do a chat GBT look like for Schrodinger? A chat GPT? Yeah. Because I mentioned earlier we talked about it. What was the question? Oh, I'm sorry. When and when do a cat GPT look for Schrodinger? What is it, like visually? Yeah, I mean, visually, you have your sessions on the left. You know, we have a few things that are kind of unique to us. You want to monitor your isolated file system because there's a lot more IP concerns in our space. I should mention that we built this entire product knowing our customers' IT preferences and policies very well, which is something that a lot of the products built generically in the AI space don't fully understand. So there's lots of customizations and hooks in the back end that make R&D IT leaders just nod their head and say, okay, yeah, you can come in. but sessions on the left file system on the right a bunch of mini apps but ultimately you're talking with Bunsen you know we do actually I believe we do have demos of it online so you can see it if you're interested and you'll say you know I'm looking at this target help me put together a virtual screening workflow and before asking that question might have been watching a one-hour video of how to do virtual screening in Schrodinger software platform and now Bunsen says these are the foremost important questions I have to ask you. If you don't understand them, let me know, and we can keep talking about it. Otherwise, answer them, and I will now go use your supercomputer to max capacity. And that type of thing, I mean, it could have been a day or days of work for someone before, and now it is a conversation like that. Now, I don't want it to sound like this technology is absolutely turnkey at this point. It still often requires expert knowledge, and we are programming as much of that as we can like we actually task our therapeutics team to put their brain into bunsen so we're not it's not just you know an llm repurposed for this we're adding a ton of knowledge to it um and there are places where bunsen will still make mistakes like every llm so it is really important we're calling it a co-scientist for a reason that the scientists still really understand their space but it's able to both guide you really well and often catch mistakes you are going to make. So if you were going to kick a weekend process off your supercomputer that cost the company $100,000 and you forgot to set the right parameter for that type of protein, Bunsen is very likely to say, hey, I think this might be wrong looking at the protein. And it just saved you $100.000. So I mean, that's the type of thing that we're super excited about, that just nothing existed in the past. Because in chemistry, there are so many gotchas. You're never going to hand code every single one into a UI. You need something with a little more sophisticated intelligence. Richie, the milestones, I want to go back to the milestones question. Are these going to be, like, are these public that will know what your milestone, potential milestones are? Like, how much disclosure can you put around that? Because, hey, getting a $70 million milestone payment is material to the company. As analysts, how should we think about these, or how should we think of modeling them? Yeah, it's a tough question. We've put out as much disclosure as we can for the terms of our collaboration agreements. So we've summarized the total milestones and, you know, the general descriptions of the royalties, you know, low single-digit, mid-single-digits, high single-digit. So we put out the disclosure that we can. Admittedly, they are, you know, not a milestone that we control directly in many cases, and so it's hard for us to guide or forecast around them. But I think the track record is really what we would point to is the persistent receipt of these over the past couple of years. And, you know, as we continue building the portfolio and as the existing portfolio continues to mature, we expect that this will become more and more repeatable, more and more recurring. But we've put out the disclosure that we can around. How important is it strategically or goals for the company to become profitable, turning it into a positive, cash flow positive? I know you've talked about facing out some operating expenses potentially. Can you just talk a little bit about when you get together, is this a goal? Is it more around building the platform and getting the revenues and the partnerships, or is it profitability? And how do you, or maybe how do you think about both of those things together? Yeah. I mean, profitability is obviously important, but it's, it's we're going to be focused on driving innovation to our customers and by investing in the platform, investing in R and D and growing the top line that will, that puts us on the path towards profitability. So that is really the DNA and culture of the company is investing in products and bringing new science to our customers. And by doing that, we also can achieve profitability. So I'd say they're equally important to us. From a balance sheet point of view, we have the cash to get to profitability and look to continue making investments that are synergistic with the platform. got it okay the um is it fair to say that the transition from on-premises to hosted software is part of this overall strategy to become a software provider and partner like is that yeah there's we um started on that journey this year um in a formal way we've actually been building towards that for several years um but it is important to us for a couple different reasons One is from a customer-facing point of view, it is we're better able to support the customer, better able to onboard the customer and also able to send trial licenses and bring new products in and have them experiment with them. From a financial reporting point of vue, you know, this is the year that we are in the midst of the transition. It's obviously caused some noise with our accounting for the year, and in particular the software revenue, which is why we're focused on ACB. But as we get through this transition and the hosted percentage increases to a place, we've targeted 75% by 2028, the reporting, the revenue reporting of our software business will become a lot more clear and reflective of the actual fundamentals of the business. can you quantify what the software products and services growth rate would be this year if not for the transition like well our ACV guidance is the clearest way to look at that so our AC V is expected to increase 12 to 17 percent this year off of 198 and how if we were to break that down how much of that like are there any components of that of the 12 to 17 is it all one bolus or oh where's the growth coming yeah yeah what's been really nice year to date and i guess for full year we've had broad growth um we've grown within top 20 pharma customers um broadly across life science material science we've Had growth from new products we've talked about bunsen predictive talks um we have crystal structure prediction is another area of growth for us um we've actually seen a better biotech market than we've seen the last couple years um from a funding perspective and also how much is biotec versus pharma large pharma is call it half of our commercial business makes sense um with top 20 pharma obviously outside of top 20 there's many large phharma customers that contribute to our overall commercial acv And new customers has been stronger this year, too. We've had some growth from new customers. We spend a lot of time talking about Bunsen. It's obviously super exciting, but I'm guessing it's a very small part of the revenue drive or associated revenue. Like we said, Bunsen for us is about throughput. It's about licenses. I know. Licenses. So that is, we're seeing license growth. And again, Bunson is still in beta. So we've announced our transaction with Bristol Myers, but it's still in beta. I think we're pleased with what it's done on license growth. But some of that growth is still to come as customers. And maybe, Pat, you can talk about this, but, you know, customers are evaluating it right now. They're going through security reviews, but that is they haven't necessarily transacted on Bunsen or increased license packages related. there's no i never thought to ask this before and i can't imagine there is but is there anything exclusive to the bristol myers agreement that others can't participate in whether it be a clinical category or any kind of proprietary not there's i mean you're you're shaking your head i assume not but for sometimes we're trying to think about what we can talk about what we cant but i know i don't think there's anything that prevents you from selling anything to another large customer that, okay, just wanted to make sure. You said Biopharma is getting a little bit better. You've seen an uptick in that. How do you see the recent improvement in those end markets driving software adoption over the next, like, is it a driver? Yeah, I think broadly the industry is embracing a predict-first computational approach to drug discovery. And that is literally the definition of our platform. So I think there's a lot of AI drug discovery discussion, you know, biology, chemistry, targets. But I think the point is, if you are trying to design a molecule to drug a target and trying to optimize across the 20-plus parameters you need to solve, our platform is the way to do that. So I think there's a broader recognition across the customer base that this is theway to do drug discovery. I think our proof points across co-founded companies and the advancement of our co-discovered drugs through the clinic are demonstrating that. So, in general, from a user-based level, there's a clear recognition of how to do this. I think from a pharma R&D perspective and even venture capital-based companies, you have to be efficient. In this environment, you has to be fast. You have to able to, you know, be efficient with how you spend the dollars. Our platform enables you to do that. when we think about the demand environment versus your execution what's going to drive growth more how dependent are you on the market how dependent are are we on your product your your ability to sell your product and the effectiveness of your of of your products yeah i mean the the market backdrop right now is it's it's a good good backdrop um and um but we are bringing new products to the market as well so i think we expect to be a you know market plus grower given the investments we make in innovation are there any products that we should be looking forward to sounds like new new products you've you obviously have been developing a bunch of things but is there any announcements we should be expecting. I'm not, you know, to give me specifics, but we're not asking for 27 outlooks here, but it sounds like there are more to come. Yeah, I mean, I think our investment in new product, it's not a destination and you're finished. It's continuous. The platform advancement, the accuracy of the platform, that will always be there. On predictive talks, we said on our call last week that we've now profiled 80 of the off targets. 80 of the off-targets is impressive. There's still a lot more to go for it to be of more use to customers. Bunsen is in early, you know, in beta right now. We're continuing to evolve that product as well. So I think, and that's just a sampling of our new products, but this is, for us, this is about continuing to advance the technology and bring in new products which can drive long-term growth for us. Well gentlemen, this has been great. Thank you so much for coming down to Florida and thank you for the time. We really appreciate it. Thank You.