Schrödinger, Inc. (SDGR) Earnings Call Transcript & Summary

All right. Welcome, everyone. This is a fireside chat with Schrödinger. Thanks for joining us. My name is Vikram Purohit. I'm one of the biotech analysts with the research team here. Happy to have with me Ramy Farid, CEO; Geoff Porges, CFO from Schrödinger. Before we get started, I need to read a brief disclosure statement. For important disclosures, please see the Morgan Stanley Research disclosure website at www.morganstanley.com/researchdisclosures. And if you have any questions, please reach out to your Morgan Stanley sales representative.

So with that, let's go ahead and get started. So we have roughly 30 minutes, got a lot of material to cover. So Ramy just, maybe we could just start with some brief opening remarks from your side on what you think some of the key inflection points have been for the business this year? And just -- for those who may not be fully acquainted to the business, just a quick sense of how the business is currently set up.

Sure, sure. Happy to. So the business is set up to be able to leverage sort of an extraordinary platform that we've been developing over 33 years. The company was founded in 1990. And this platform has allowed us to compute properties of molecules with very high accuracy. So we have this platform, developing it over a long period of time and we leverage it in several different ways. One way is to actually make that software available to the industry. And by industry, I mean the drug discovery industry. So pharma companies , biotech companies worldwide, everywhere. We have sales offices all over the world. Also to material science companies doing all sorts of different things that involve designing novel molecules and where it's really beneficial to predict the properties of molecules before you go randomly making a molecule and spending all that time and money doing things by trial and error, just using experiments. The other way that we leverage that platform is to help other companies to advance their drug discovery programs or material science programs, so where we are using our software internally and helping to advance those programs. In those types of collaborations, the partner has a lot to do about, for example, in drug discovery, picking the target, making decisions about when to take the program to development candidate stage, when to take into the clinic. But we're playing a big role and in fact many of these companies, we've cofounded. So we were involved in the formation of those companies. And that business also generates revenue in the form of upfront payments, in the form of preclinical milestones, clinical milestones, royalties on sales -- and that's -- so that's interesting, another way of monetizing the platform. And then more recently, we started to work on our own programs that we have retained, all the value and all the ownership. So they're wholly owned programs. Some of them are partnered but many of them are wholly owned. And of course, there, we are selecting the targets, advancing the programs, taking them to development candidates, taking them into IND-enabling studies and even more recently, taking them into Phase I clinical trials. We have 1 program that's there now, another one that will there soon and a third one where we plan to submit an IND soon. So that -- I hope that helps understand. And I'll just say one last thing, just a few seconds. It's very important as we're having this discussion to remember, these are not 3 separate businesses. There are tremendous synergies between these businesses. We're always looking for new ways of leveraging those synergies. And so it's a very deliberate thing that these are all in the same company and benefiting from each other and sort of growing together.

And Vikram, you asked about this year. We started the year with a major validation event, which was the distribution from Nimbus after the sale of TYK2 to Takeda. It was really nice to get $147 million and that left us in a really well capitalized position. The second thing that we've signaled is that we are seeing opportunities for really substantial growth in the software business, despite some uncertainties in sort of biotech funding environment that we're all aware of. Our largest customers, in particular, are having conversations with us about significant step-ups in the deployment of our technology into their drug discovery organizations and those discussions are continuing. We are advancing our collaboration. As I had signaled -- we talked about in many calls, we are transitioning our R&D expense and our internal teams from the collaboration programs to proprietary programs. And that's what we're increasingly we're going to start seeing data from. We're a clinical company this year for the first time, which is fairly remarkable. We now have 2 programs that are in Phase I. We have 2 clinical trials with MALT1, 1 trial that we're just opening up now for CDC7. So the company is definitely transitioning. We're not walking away in any way from the software business. In fact, we see lots of opportunities there but we're definitely adding on and investing in the proprietary medicines as well.

Got it. Got it. That's helpful. And there are 2 kind of broader macro issues that I wanted to get your perspective on before we discuss the software business and the drug pipeline because I feel like having a perspective on that setup of what's going on, where broadly it's going to be additive to that discussion later on. So I think one of those questions strikes me like 1 for Geoff, one strikes me like 1 for you maybe, Ramy. On the topic of biotech funding -- and both of you feel free to chime in. But what has been the impact -- or what is generally speaking of the impact of biotech funding fluctuations on Schrödinger top line? And how can you best try to structure the business to insulate from these kinds of cycles, if that's possible.

Yes. If you look back at the growth rate of the software business. I think last year, we grew at 21%, the year before, I think it was 30% or so. And the year before that, it was close to 40%. There's no doubt that in 2020 and 2021, there was some extra growth contributed by emerging companies, newly funded, saying, we're going to do drug discovery against target X or against class of molecules Y, or in some disease and -- and they opened up their drug discovery efforts by signing contracts with us. So new companies emerged in those years that definitely contributed. Now if we look at the number of new companies showing up, it's gone substantially down. But the companies that took on our technology then, by and large, have stuck around. Now not all of them, there's been some attrition. There's been some consolidation. There's been some acquisitions, small companies, sort of going into big companies or shut down discovery. So there's been some small amount of attrition there. But the biggest change is that we're not seeing the new, new customers showing up. But another interesting thing, the companies that seem to have made it and I don't know off the top of my head when they went public, whether they were in that 2021 cohort or slightly before that. But I'd say Morphic is a good example of that. The companies that sort of got out into the clinic seem to continue to be well enough capitalized to not only afford our software but in many cases, to be increasing their adoption of our software. So amongst our top 20 customers, there's definitely biotech companies in there, it's remarkable that we have biotech companies who are using on an ongoing basis, more software than some of the largest pharmaceutical companies in the world. Which gets to the question that we're always asked about, what's the opportunity? The opportunity is for those big pharma companies to step up. So that's kind of what's going on with the funding part. It's not that people are dropping off. It's not that we're losing a lot of customers but it's the absence of the new, new customers that's causing our growth rate to be lower than it was in the past.

Understood. That's helpful perspective. The second question then is related to all the recent focus on AI and ML across every sector, including drug development. I think it would just be helpful to get your perspective on, as people think about companies like Schrödinger and other AI enabled-biotech players, for the lack of a better word, what are some key nuances to keep in mind if you think about what's really possible versus where expectations might be on any given day? And where do you feel like AI/ML tech-enabled drug development is truly validated and has a true use versus where it's still aspirational?

Yes. So I think the most important thing to start the conversation is to remember that AI/ML is actually a technology. It's an algorithm. It has, like every technology has, a domain of applicability, has things it's really good at and it has things it doesn't. I think the problem is with LLMs in particular, when that came out, that to a lot of us, including me, by the way, it doesn't matter if you're an expert or not, when you start to use these technologies, they start to have -- they take on characteristics of being a bit magical, right? It's sort of like you can't even imagine, how the hell is this working? And you have to realize, by the way, in that case, it's just trained on a massive amount of data. So yes, it's doing what it's supposed to do. It's -- but it's an actual technology. You can understand it. It may take a little extra time. But that's what it is. Again, what that means is it has a domain of applicability. There are certain things, it's going to work in really well. And there are actually certain things that it won't. So what that means and what -- there's a way of describing that now in very general terms. In cases where you want to predict something that looks very similar to things you already know that works really well, that's what machine learning is. In cases where you want to learn about something or make a prediction about something that's completely novel, for example, that's not in the training side of these systems, it will not work. It doesn't matter all the sophisticated words you would assign to a generative, deep learning, whatever the fancy word of the time is -- it still is -- that's what I mean by it's a technology. Don't let yourself be fooled by the kind of magical talk around it. Okay. So now where is it having an impact? It's having a huge impact at Schrödinger. We have quite a number of programs that we've been working on. A large number of them are in the clinic. I mean, these are advancing. We have a really fantastic track record. Well, it turns out that we have been leveraging physics-based methods, that's the other type of method where you're using first principle, nothing to do with machine learning, with machine learning in a way that actually leverages the advantages of machine learning. Machine learning has advantages and disadvantages. The disadvantage we are just talking about that needs a training set. Where is that training set to come from? It can actually come from ab initio physics-based methods because you can't produce large training sets by just doing a lot of experiments. That would take hundreds of years. That's not practical. But you can do hundreds of years' worth of experiments in a day using these physics-based methods that we produce. So there's an example where it really is having an impact. It's allowing us to explore a huge amounts of chemical space by understanding the technology and leveraging the advantages that each different technology has. The companies that are -- and there are a lot of them, hundreds of them that are claiming they have a black box solution. They can't tell you much about. They can't tell you where the data comes from. They can't tell you about the algorithm. They have no track record. There's nothing like, I think it's okay to be skeptical of those, even though the words sound really fancy when they talk about it. It's okay. Your instinct is correct. It's probably nothing there, yes. Now this is, everything I just said is in chemistry. okay? I'm talking about chemistry. There are many other aspects of drug discovery. There's drug development. There's patient selection. There are things on the other side, biology, path -- understanding pathways, these are areas that actually turn out to be quite amenable to machine learning to even things like LLMs. That's not a space we're in. We -- since we understand the technology, we can say a little bit about it. There are some very interesting things going there that use the same principles. Like if it sounds too good to be true, it probably is, if it's a black box and they're not going to tell you where their data is coming from to train it, again, it's probably a good idea to be skeptical. But there's some really, really interesting things happening outside of what we're working on. Again, in the biology and understanding that and reading literature and doing what about -- just and, of course, in informatics, which is really quite amenable to machine learning because there's a lot of data. That's what it's always about. It's about how much data you have and if it's good data. I hope that helps.

Very helpful. I think that's a good segue into talking about the software business. So with your last earnings update, you had increased your software guidance. That was based on visibility. You noted having into some potentially large contracts coming up. And I think this has been a topic of focus for a while now. How much visibility do you truly have into your business backlog? So could you just kind of peel that a part for us?

Yes. I think I'll say a few things and hand over to Geoff and both of us are thinking a lot about this. So first of all, it's important to remember that we have a very, very high customer retention rate. I mean it's essentially 100%. The few customers that don't renew are ones that stopped doing research or get acquired by other company -- so they're very, very large, essentially 100%. So that already provides some level of, all right, transparency going. They're going to have to renew. They become dependent on the software. So it's not all this uncertainty, gee, I wonder if they're going to continue to use the software. The other thing that we're really encouraged by is that we have noticed that when we start having conversations with heads of research, right, they're people who are actually really making these decisions and are really thinking about the efficiency of the company versus sort of maybe if you go too far down, end users just trying to get through their day and they're more focused on does this software easy to use or not, things like that. Anyway, when you're talking to the heads of research and they're saying, "Hey, we're seeing the success of the TYK2 program and the alpha 4 beta 7 program from Nimbus and Morphic and so on in the GLP-1 program from structure, we want that, that's important to get in turn and then they're saying, okay, not only do we want that but we're prepared to have you train us because we provide a lot of training. It's something we've really invested a lot in -- that's a pretty good -- that's starting to feel pretty good, right? If they're coming to us for training, they understand that the technology is having an impact and they're just trying to figure out how to get over all the internal barriers. IT barriers, knowledge barriers, that starts to give us some comfort that, okay, there's a customer that's saying, "All right, we need to be using this technology at scale. We need to be using it the way Schrödinger is using it." That's where the visibility comes in.

Yes. And as you can imagine, Vikram, we have a database of every one of our customers and we now when their contract is up for renewal. And they will know if they haven't renewed it because they're going to get switched off. And then they won't be able to be testing any molecules. So we have a commercial organization that's in conversations with those customers, the ones who are spending more than a few thousand dollars a year. Your contract is coming up in September or in December and we start the discussion. And then they say, okay, this is how much we've been using it. And typically, there's a step-up in that contract. But more importantly, there's certainty about renewing it. And as we've said in the past, a number of them, the largest companies elect to have multiyear contracts with us. They may have building a business process, a fundamental business process and an organization around using a particular technology, they need to know that's locked in. So they say, okay, we want guaranteed pricing, et cetera, for 3 years or 2 years. And as you know, the revenue recognition for that forces us to recognize that revenue upfront. But we can see that renewal coming years in advance. And short of the company sort of being acquired and shutting down drug discovery, it's pretty inconceivable that they will engineer a business process and organization and then stop using the technology. So we have a lot of visibility to those renewals of those multiyear contracts in particular but all of our contracts. What we don't have certainty about or even really tight confidence around is how much they'll step up. But the trend across our business is the deployment of our technology is scaling up as the customers get more and more experience with it and gain more and more utility from it.

Got it. Got it. Okay. That makes sense. Okay. Building on something you just said, Ramy, when you're speaking to heads of research about their decision-making around the use of Schrödinger software -- all the time you -- something you said previously about the TAM for Schrödinger software being much bigger than what it is currently, right. And that all implies that there are use cases within biopharma that are underutilized, where Schrödinger software services could be a plug-in, where they're currently not plugged in. What do you think are a couple of good tangible use cases where it's just not being used as frequently they should be. Yes.

Okay. Here's a good example. So we would estimate that of all the programs that pharma is working on, about 10% to 20% depending on the company, I would call structurally enabled. In other words, they know the structure of the target. They know the identity of the target, they know the structure. They have a lot of details about how the molecule binds. And they got that from experimental methods. They went, they got lucky, they solved the structure pretty quickly using X-ray crystallography, that's good. And then -- and that's great input for the -- for our software and they get great results with those. Okay. What about the other 90%, 80%, 90%? Well, it turns out there is ways of actually enabling those targets that requires a lot of work. You may have to go out and get a cryo-EM structure. You may have to go get an AlphaFold structure and then refine it to higher resolution using our platform. It's a lot of work. It's pretty complicated. Protein structures are complicated. Every time a molecule binds to a protein, the protein adopts a new confirmation, these are complicated. And you have to understand not just the structure of the atoms and all the atoms in the protein, you have water molecules associated with them. I mean this is an enormously complicated flexible thing. But it takes a lot of work and a lot of licenses of the software, a lot of technology and a lot of knowledge to enable the target. But if you're motivated to do it because you know that if you have a structure, you're going to be able to leverage our platform at scale and that's going to have a huge impact. So I think this is an area where the work that it takes to structurally enable programs, even picking the target. So you will find in pharma, this is all [ segregated ]. Now, biologists pick the targets, hand it over to the chemists and the chemists say, "Wait, there's no structure, say too bad." We need to -- but we -- that doesn't make sense. So at Schrödinger, these teams are all working really closely together and we're saying, okay, what is going to be the path to identifying a great target by getting a structure and doing all the work that's required. And that has a profound impact on our business because once, of course, customers are enabling more targets, right, now the demand for the software really increases substantially. So it's an area where we're focused on. They're not -- this is an area we're not utilizing -- [indiscernible] really quickly, I know we're running out of time, is they don't have enough licenses to actually explore enough chemical space. I'll just say this, drug discovery, I'm going to say some profound statement that you may not have ever heard before. Drug discovery is really hard. So it turns out really hard. And what does that mean? It means you have to test huge numbers of molecules to find that perfect molecule that some -- by some miracle, right, is potent enough and selective enough and soluble enough and permeable enough and doesn't hit anything else. I mean that's really hard. And the way around that obviously is to explore a huge number of molecules. That requires a lot of licenses and a lot of commitment to using the technology at scale. They're not doing that either. But a few companies are. That's the exciting thing, they're starting to get there. They're doing on a few programs. And we're pretty sure that once 1 company, 2 companies, 3 are doing that, of course, they all will do that if it's just going to take time. So that's another really great example of -- I think that's what you're asking.

Got it. Got it. That's helpful. We have around 7 or 8 minutes left. Maybe it's a good time to pivot to another line item in the business, drug discovery and partnership revenues. There's been a good amount of focus on that throughout the year. My first question for you there, Geoff, just to level set for all of us, just walk us through exactly how that line is estimated because there's obviously a good amount of gray area in when milestones could be achieved and when they could come through, so just kind of unpack that for us first.

Yes. No, it's complicated. So the revenue that runs through what we call Drug discovery is sort of a combination of different things. That's -- it's -- our portion in that quarter revenue from upfront payments that we are eligible to recognize in association with completion of work on that program. And that's based on estimates of the time required complete all of the work in the program and that revenue is sort of spread across that period. So as that time changes, so it goes from 4 quarters to 6 quarters, then the amount of revenue that you get to recognize goes down by roughly 1/3 in each quarter. So the first thing is, we have to recognize the revenue from the upfront payments. Then we have to try and pick when the programs that are still in our portfolio in the collaboration are going to get to a certain milestone that would trigger a payment. So that might be getting to DC [indiscernible] or somewhere along the discovery pathway. And then we have to assume we will meet the criteria of the partner. So we have to estimate the likelihood that they will say, yes, a check, looks good. And that, that will then trigger a payment. Then there's a third component, which is the programs that -- the ships that we've set forth on the ocean that are in our partners' portfolio is in active development, we have to estimate when they will hit some sort of milestone or kind of figure, will figure a milestone payment. So as you enter clinical trials or enter a Phase II or complete a Phase II depending upon the agreement and in many cases, those are with third-party companies, meaning we did a collaboration with Company X and they've then partnered the program with company Y. So we have to try and estimate when company Y will reach that end point. So there is an increasing amount of uncertainty, the less it is in our control in those estimates. And the last piece that makes it particularly problematic is that milestones get bigger and bigger, the less control we have, sadly, because as the programs advance into the clinic, as you know, the industry norm is that the milestones get larger and larger, $25 million, $50 million, sometimes even $100 million down the fact. And we don't have much control over that at all because that's in the hands of the ultimate licensing company. So all of those factors contribute to the uncertainty about estimating the drug discovery revenue.

Got it. Okay. That's helpful. And building off that, then, if you could talk a little bit maybe about your partnerships and recent progress there, with BMS, really you have partnerships with both of these companies. What would you cite as some of the most recent kind of concrete developments to the extent you can talk about them? And looking forward, what do you think is realistic in terms of the scope of disclosure you might be able to make as these things move forward?

Yes. So well, certainly a highlight for the year is the SOS1 program, reaching DC and then transitioning to the BMS' development portfolio. We understand that's going ahead pretty actively. But again, we don't get any more information how that [indiscernible]. But that's pretty exciting. That's the first of the 6 programs in the BMS collaboration, that triggered a $25 million milestone payment in Q1 that we disclosed. And we're continuing to work very actively on the rest of that portfolio. So it was nice to see that collaboration that's 3 or 4 years old, now reaching that kind of milestone, particularly given the investment that we've made and all the efforts we put in. The Lilly collaboration is early. We only announced that at the end of last year. It's a pretty, I would say, high-profile, high-value target and it's going really well. We think there are opportunities that we can do more collaborations with not just Lilly but even with other companies. And what we're continuing to say to them and saying to our investors is that we do have a limited bandwidth for taking on new collaborations that -- as we're better and better capitalized and more and more mature as a company, we're shifting our resources towards our proprietary programs, where we retain 100% of the value. So why should we be discovering really great molecules for a partner where we're only retaining 10% of the value. So there's definitely that transition. We do believe in the collaborations. We're committed to them. We think that we'll continue to have collaborations but they're not going to sort of really ramp up. We kind of think that there's going to be a steady level of collaborations going forward. Maybe we'll add some, maybe some will fade away. But that's more and more we'll be doing our proprietary programs.

Yes. Perfect. Exactly. Got it. So since you mentioned that there's kind of a cap on what you can do in terms of partnership capacity. How are you filtering what interesting, what's additive to Schrödinger? What's worth pursuing, what's not?

Yes. That's evolving. In the early days, we didn't have as many capabilities. We didn't have a structural biology lab. We didn't have -- we had 1 biologist in the group. We didn't have CMC people and so on, their -- those capabilities are evolving. And so what we're looking for -- and I think we have found it in a few partners is, when a partner brings something to the table, right, that really is difficult for us to replicate. I mean, I think Morphic is such a good example. I mean this is a company that is world-class understanding of [indiscernible] structure from Tim Springer's Lab and not a company has that. I mean, it's absolutely incredible, right? Their ability to understand not just the structure but the structure function relationship. And that's really, really hard. That's a hard thing to replicate inside Schrödinger. That's an example where it makes sense to form a partnership, a GPCR company structure therapeutics, perfect example, right? GPCR structure is getting more and more routine to get structures but still really complicated, understanding the biology, understanding the structures of these targets is still quite complicated. And so in examples like that, where there's a real complementarity between the technologies. But again, I think it's kind of exciting that, that's evolving and we're gaining more and more capability. So I think there are more and more programs that we can take on from the beginning, from the target selection all the way to IND and even into the clinic. So that will continue to change. But we think there's still -- there's some very smart people out there that are doing some exciting things and they've been working on problems for 20, 30 years, that's a little hard to replicate in a ...

Sure understood. We are just about out of time. I'll ask you a final question. We didn't get a chance to talk much about the proprietary pipeline. But just to close out, maybe you can just recap some of the key data points we should be looking forward to in the next 6 to 12 months from your pipeline programs at the moment?

Sure, so we have 2 programs in the clinic MALT1 and CDC7, they're both in the hematologic malignancy indication, both in Phase I studies, for MALT1, we're actively enrolling both the healthy volunteer study to get a lot more PK/PD, safety tolerability data and also the patient study, we've expanded the study sites in Europe in B-cell malignancy. We think that we'll have some data, at least from the healthy volunteer study sometime this year. And that should be really interesting, particularly given the context of some data from competitors already out there. The CDC7 trial, we're just opening study sites now but there's a lot of enthusiasm for that trial, it's a tough indication, AML, we all know, it's very challenging to develop novel medicines in that but there's plenty of unmet need there. And then we're on track for filing the IND for the [ V1 ] next year. So a lot going on there, we will have a Pipeline Day at the very end of the year in December after we get more data from the MALT1. And hopefully, then we'll share some other things in the pipeline that we haven't disclosed before.

Great. Let's close out with that. Geoff, Ramy, thanks so much for joining us. We had a great discussion. Yes. Thanks.

Thank you.

Thanks, everyone.