Schrodinger at KeyBanc Forum: Computational Drug Discovery Focus

On Tuesday, 18 March 2025, Schrodinger Inc (NASDAQ: SDGR) presented at the KeyBanc Annual Health Care Forum 2025. Led by CEO Rami Fareed, the discussion centered on the company’s innovative computational platform for drug discovery, highlighting both advancements and challenges. While the company exceeded financial expectations last year, it faces the ongoing task of balancing internal and collaborative programs.

Key Takeaways

• Schrodinger’s software revenue grew by 13.3% last year, surpassing initial guidance.
• The company projects a significant increase in drug discovery revenue by 45% to 50% for 2025.
• Operational expenditure growth is forecasted to be less than 5%, indicating operational leverage.
• The transition to hosted software is underway, with hosted revenue rising from 13% to 20% in 2023.
• Schrodinger plans to present clinical data for its three most advanced programs throughout the year.

Financial Results

• Q4 Software Revenue Growth: 16%
• Total Q4 Revenue: $88 million
• Full Year Software Revenue Growth: 13.3%, exceeding guidance of 8% to 13%
• 2025 Software Revenue Growth Guidance: 10% to 15%
• 2025 Drug Discovery Revenue Growth Guidance: 45% to 50%
• 2025 OpEx Growth Guidance: Less than 5%
• Increase in customers spending over $5 million: Increased from 4 in 2023 to 8 in 2024

Operational Updates

• Gates Foundation Grant: Nearly $20 million to advance toxicity prediction technologies.
• Biologics Platform: Released a platform built on the Live Design small molecule informatics platform.
• Machine Learned Force Fields: Enhancing physics-based methods, with applications in battery design.
• The company can predict the affinity of an antibody to an antigen and compute thermal stability.

Future Outlook

• SGR1505 (MALT1 inhibitor): Data presentation expected in Q2 of the current year.
• 02/2021 (CDC7 inhibitor): Data presentation in the second half of the year.
• Fivefifteen (MIP1 inhibitor): Data presentation later in the year.
• Predictive Toxicology Platform: Anticipated release later this year.

Q&A Highlights

• Large Pharma: Opportunity to increase software usage and spend, currently a small fraction of their R&D budget.
• Small Biotech: Growth continues due to the software’s efficiency and cost-saving benefits.
• Hosted Software Transition: 20% of revenue from hosted solutions in 2023, smoothing out revenue recognition.

Readers are encouraged to refer to the full transcript for a detailed understanding.

Full transcript - KeyBanc Annual Health Care Forum 2025:

Scott: Welcome everyone. Thanks for joining us. We’re kicking off our virtual healthcare conference today and kicking things off on the healthcare technology side. We have Schrodinger here. We have Rami Fareed, CEO, joining us today.

And Rami, I believe that you wanted to present some slides first and then we’ll dive into Q and A. But thank you so much for joining us, Rami.

Rami Fareed, CEO, Schrodinger: Of course. Yeah. I’ll just give a present a few slides, just high level overview. Should I go ahead and get started?

Scott: Yes. Let’s kick it off.

Rami Fareed, CEO, Schrodinger: Perfect. Yes. So let me start by first describing really what we’ve developed over the last thirty five years and our platform and what we’re trying to do, what the goal of of of the company is. And so the goal is to essentially develop computational methods that replace experiment, expensive, slow, unreliable experiments. And really, the goal is to predict at the highest level to predict the properties of molecules.

Obviously, if you can do that, design molecules and predict their properties, that will have a pretty significant impact on the ability to design molecules. It should improve the performance, get there faster with better molecules, improve probabilities of success. So over the past thirty five years, we’ve been developing methods for accurately predicting the properties of molecules. There are, at the highest level, two ways of doing that. One is using first principles, using physics, and the other is using machine learning or artificial intelligence.

And it turns out both of these are pretty powerful methods, but they have limitations. The physics based methods that we’ve developed over the last thirty five years are incredibly accurate. It’s pretty extraordinary. This is something that, you know, you can imagine it’s not so straightforward to actually simulate, for example, a molecule binding to a protein and all of the atoms associated with that with that process and compute from first principles the binding affinity, for example, of of a molecule. But we’ve done that.

It’s highly accurate, but it’s computationally expensive. And in the context of a drug discovery project where hundreds of billions of molecules need to be explored, there aren’t enough computers in the world to be able to do that in a reasonable amount of time. Now machine learning has the advantages many of you have heard of being pretty efficient, fast. But it has this other limitation that everybody is very well aware of, which is that it requires, and in the case of chemistry, very significant training sets, very large training sets because of the diversity and complexity of chemical space. So what are we gonna do?

We’ve got this fast method, but you kinda it it’s it it by itself, without, robust training sets, without large training sets, it has this limitation. Any of these physics based methods that are accurate but slow, obviously, if you combine them, you can do something pretty extraordinary, which is you can use these physics based methods, these first principles methods, to produce very, very large training sets, the kinds of training sets that are essentially impossible to produce using experiment. We can, in one day, using physics, produce the equivalent of ten years of experimental data. And now you can imagine how powerful this method is where you can use physics to generate large training sets, and that essentially allows AI and machine learning to actually have an impact in in drug discovery by combining both of them. So that’s the platform we’ve been developing over the last thirty five years.

So now how are we applying, if you will, this platform? As I’m sure everybody listening understands and knows, we license our software to life science companies, drug companies, pharma and biotech, globally, and material science companies. Material science, life science applications are, of course, very different, but the fundamental physics, of course, is the same. And then you can see we have quite a number of customers worldwide. We also use the platform in collaborations, both drug discovery and materials design with, in some cases, companies we’ve co founded and in some cases with large pharma companies.

You can see there, we have quite a number of collaborators. And more recently, we initiated efforts to advance proprietary pipeline. We’ll talk about those in a second. And those are in discovery and there are a few in the clinic. And you can see there, we have more than eight active programs.

And you’re gonna see as we’re talking about this, and I’m sure in the q and a, these are highly synergistic, all three of these. And again, I think we’re gonna talk about those in the in the q and a. So let me just give a quick highlight of of the last year. You can see in the fourth quarter of last year, we had quite a good quarter. You can see there that the software revenue grew by 16%.

Total revenue was $88,000,000 and you can see the drug discovery revenue. The full year was also quite a good year. We exceeded expectations. Software revenue growth was 13.3% above the guidance that we gave, which was 8% to 13%. And you can see there the total revenue.

Maybe more importantly, our guidance for 2025, you can see there that we’re guiding to software revenue growth of 10% to 15% this year. And you can also see that we’re guiding to quite a significant increase in the drug discovery revenue, 45% to 50%. And you can also see there that we’re also gaining some operational leverage. You can see there that we’re guiding to less than 5% OpEx growth. Now I think we’re going to talk about this in the Q and A as well.

This is very important. We’ve managed to expand existing collaborations. You can see there with Lilly and Otsuka. I think that’s an indication of the success of the existing programs that we’re working on together with them. And we also have reiterated our guidance that we gave last year to presenting clinical data this year for our three clinical our most advanced programs, our three clinical programs, fifteen oh five, two thousand nine hundred and 20 one and three thousand five hundred and 15.

In particular, SGR1505, our MALT1 inhibitor, We’re going to be presenting data in Q2 of this year. We also are pretty excited about the fact that we continue to advance this platform. We have quite a significant effort in this area. We’ve announced a initiative which is funded by the Gates Foundation with a rather large grant, nearly $20,000,000 grant to advance technologies for predicting toxicity of molecules associated with binding to off targets. We’ve also announced the release of a biologics platform that’s built on our small molecule informatics platform called Live Design.

So we’ve released a version of Live Design that supports biologics. And we’re very excited about the third thing that’s listed there, the machine learned force fields, that’s allowing us to continue to enhance the accuracy of the physics based methods that we talked about earlier. And in particular, there’s some very exciting applications in battery design with regard to this machine learned force field that’s allowing us to simulate chemistry battery at the electrode electrolyte interface. And I’ll just leave leave us on this slide. I think I’ve gone long enough.

We should get to the q and a. I wanted to give a sense of how we’re advancing the platform in all of these different areas. As much progress as we’ve made and it’s been pretty extraordinary over the last thirty five years, we continue to advance the platform as a very important part of the business. We’re the innovator in this space. And so you can see here everywhere from target validation to hit discovery to lead optimization even to preclinical development, we’re advancing technologies that are having quite a big impact, not only for our customers, not only our collaborators, but of course on our proprietary programs.

So I think with that, we should probably launch into the Q and A.

Scott: Thanks, Rami. That was super helpful. And I think for investors that are not less familiar with your story, I think that was a great sort of comprehensive overview. Just as a reminder, investors, if you’d like to submit a question for me to ask, there’s a link at the bottom. I’ll see it on my end here and I’ll be able to ask Rami the question.

But I guess I’ll kick things off here, Rami. Maybe talk about end markets and your customers. What are you seeing in the market currently, both from small biotechs all the way up to large pharma? Obviously, you’ve put really healthy guidance out both in the software and drug discovery side, but kind of walk us through the customer segmentation. I think we get that question a lot, especially on the biotech side and the potential recovery there.

Rami Fareed, CEO, Schrodinger: Sure. Yeah. First, with regard to large customers, I think you can you know this that we of course, all large pharma companies are using our software. That’s great and have been for a long time. But there’s a pretty big difference between the amount of software and therefore the spend that some of our largest customers among, let’s say, the top 20 or top 30 pharma companies, there’s a very large disparity.

And that, of course, is a huge opportunity. I think you saw in our KPIs, we increased the number of customers spending over 5,000,000 in ’24 from ’4 sorry, in ’23 from four to eight in 2024. That’s a that’s a pretty big increase, but that’s only eight. And there are quite a few that are still below that. So we see a very nice trend of large companies significantly increasing their usage of the software, because that’s really where you see an impact.

The more molecules you can explore computationally, which requires more licenses, which requires more spend, the bigger the impact and the more likely you are to actually identify a molecule that has all the properties that require. So that’s on the on the large company side. Oh, I should mention one other thing. The spend on our software relative to their R and D spend is, for our largest customers, is around 0.1%, zero point one five %. So it’s still a small fraction.

So again, I think that’s a indication of the significant opportunity on the large company side. On the small company side, now this is important. Yes, there are challenges, of course, that are happening. But among our largest customers are biotech companies. They’re scaling up their usage as well.

They’re using our they’re spending on our software a larger fraction of their R and D spend, which goes back to the earlier point about the opportunity.

Scott: Yeah.

Rami Fareed, CEO, Schrodinger: But yeah. So we still see growth among the small companies. And here’s the reason. This is kind of important. I hope this came across in the presentation.

The application of our technology, especially as you scale it up, is improving efficiency. It’s reducing R and D costs. It’s not increasing it. And you’re getting

Scott: into that as a small lot smaller biotech than the APS pharma. Yeah.

Rami Fareed, CEO, Schrodinger: Yeah. Yeah. Yeah. So so I I think I think that’s a pretty important point. So in in an environment where there are these sort of challenges, macro challenges with regard to R and D budgets, that that actually results in an increase in in the demand for the software because not only does it just accelerate discovery programs, but it improves.

And we’ve talked a lot about this. It increases the probability of success, which also reduces the cost. If you keep having failure after failure, of course, those those costs, you know, accumulate.

Scott: That’s super helpful. So I guess maybe just breaking down your two revenue streams on the software side, your guidance of 10% to 15% for this year. Maybe just walk us through the assumptions on that. For investors that are unfamiliar with Shorting Your Story, there traditionally, historically, you’ve done multi year on prem software agreements, which tend obviously to hit in terms of revenue recognition all upfront or large portion upfront. We’re moving towards sort of annual cloud based streams.

Maybe talk about the moving parts there, what’s embedded on the low end of the guidance, what’s embedded on the high end of the guidance?

Rami Fareed, CEO, Schrodinger: Yes. As you mentioned and I mentioned too, we’re guiding to 10% to 15%. We have a a we’re very confident in that range. It’s broad based. There are many ways of achieving that.

But let me now address your specific question about on prem and hosted. Correct it’s true that the majority of our revenue is on prem, which means that most of the revenue from the deal is recognized in the quarter that it closes. And because our contracts are tied to budgets in pharma companies and their budgets are typically tied to the year to the fiscal to the calendar year, we have a rather large Q4. And I think if you look at our quarterly revenue, you can see that it’s quite large in Q4, smaller in Q2 and Q3 because there just aren’t that many renewals there. And again, if you have most of the revenue being recognized in the quarter the deals close, you will tend to have larger Q4s.

You also have larger Q1s, and then Q2 and Q3 are lower. And we’ve explained that over and over again, but I understand that it’s a little disturbing and a little bit hard to follow because the revenue sort of going up and down. We we actually present a a trailing, you know, q four twelve month a a trailing, average of the the quarter, and you can see that smooths it out. But another way to smooth it out in for you know, in a in a gap way is, to is this, as you said, this sort of transition to hosted. Now I think it’s very, very important for everybody to understand that when we say hosted, it’s not the traditional sense hosted.

That is, we’re not all of a sudden hosting all of our customers’ software and thereby sort of increasing the cost charge. All that’s being hosted is what’s called the license server, which just monitors the licenses that are being used, not the actual underlying code. That’s still on prem, so to speak. But Yeah. It doesn’t matter.

If you’re hosting any part of the software, the whole entire thing is considered hosted. Okay. So now what does that mean? That means that now, of course, the revenue is recognized ratably over over the year, and that tends to smooth it out. And you saw that we’ve increased the percentage of revenue that’s hosted in ’23 from 13% to 20%.

So it’s increasing. We’re doing it slowly because you can imagine, if you do it too fast, now you create another problem, which is the in the year that you’re undergoing the transition and a number of well known companies have done this, the revenue drops. So we’re doing something pretty incredible, which is we’re shifting the revenue to hosted, but we’re doing it in a in a sort of controlled way where we continue to see growth in the business while we transition to hosted in. And that will have a tendency, of course, to smooth out the revenue over time, but it’s a gradual process.

Scott: That’s helpful. And maybe provide some color there, Rami. How are those contracts or how the negotiations or behavior of customers in that progression, how was that all what are the logistics behind it? Are you pushing it? Are they sort of requesting it?

Can you walk through those customer dynamics?

Rami Fareed, CEO, Schrodinger: Yes. So to the customer and really to us, the two are nearly identical as I was sort of hinting at. We’re just hosting the license server. So the experience for the customer is identical. The only difference is that it removes a little bit of burden on the first day that the licenses start up from their IT departments because they don’t have to spin up the license server.

We just take care of it. And so we when we approach a customer to say, hey, maybe we can help you with this, and this might help you because now we’re gonna understand what licenses you’re using, and we can help you make sure you have the right licenses. So that’s sort of helpful. We’ve had very little pushback when we talk to customers about that transition. But as I said, we’re undergoing that transition sort of slowly.

So so it’s it’s essentially under our control. When we ask a customer to switch to hosted, generally, that’s well received. And so it’s more in that’s more the direction that we’re doing it. It’s not like there’s sort of demand coming from customers. There’s again, they don’t really know the difference except for maybe a few hours of IT work at the beginning of the contract.

Scott: Great. I guess, I’m gonna shift the conversation over to, I think, the most important sort of milestones that you guys have seen recently and that’s these are these what we call, like, combination deals, these large collaborations with Novartis and Eli Lilly. Can we talk about that more, what that means to your the proof of concept for your platform really and where you expect do you expect more of these large combination with large pharma over time?

Rami Fareed, CEO, Schrodinger: Yeah. That’s a great question. So first of all, let me explain sort of one of the big challenges that we have. I I hope that this is something that’s coming through that the application of our platform at scale is a completely new way of doing drug discovery versus sort of traditional trial and error, essentially, you know, relying solely on experiment. We know what we know how productive that is, not terribly, and we know about all the failure rates.

But that is as is the case in every other field that’s been transformed by computer aided design. There’s a period of sort of disruption, and there’s a period where nobody knows how to really undergo that change. You don’t have the expertise to do it. You don’t have the culture. You don’t there’s there’s and, again, you can look at other industries, even even animated movies and airplane design and, you know, all these industries that have been completely transformed by CAD.

If you look at the history of those companies, you’ll see the same thing. Nothing different here. What does that mean? That means that there’s a challenge in actually training companies to use the software at this scale. And there are two ways of doing that.

One is just training, and we do that all the time. We have a very, really incredibly talented education team at Schrodinger that does workshops and all sorts of training sessions. We have online courses, which are very, very well attended. But there’s another very effective way to do it, which is just work together with a company on a project where they can see they have a front row seat, as we like to say, to the collaboration. They can see how we’re doing it directly.

They can see what it means to run a very, you know, provision a hundred thousand nodes on a on a on the cloud or, you know, GPUs on the cloud and run things at this sort of large scale. And when they when they see that firsthand, it’s a very, very effective way of that knowledge transfer that’s required to get companies to to adopt the technology at a large scale. So we really like these these these combined collaborations. It results in not only a very exciting and an important drug discovery collaboration, and you can see that we talked about the revenue from those and and the value that’s been generated from those has been quite significant, not only from revenue, but also from even the equity stakes we have in the companies we’ve cofounded and so on. But at the same time, it’s also very effective way of transferring the knowledge, which is resulting in the companies really adopting the technology on a very large scale.

So I think you asked, are we going to continue to do these? Yes, absolutely.

Scott: So moving on here, maybe talk about the and you had a slide on your new platform. So I want to drill in more on the predictive toxicology platform.

Rami Fareed, CEO, Schrodinger: Might as well go back to the slide, right?

Scott: Yeah. Let’s go back to it.

Rami Fareed, CEO, Schrodinger: Right here.

Scott: Yeah. Yeah. There you go. You know, I think this when was this introduced last year, Rami? Is it fully Well,

Rami Fareed, CEO, Schrodinger: the the grant was announced last year. We’ve been working on it. That that’s when we started to work on the project. It hasn’t been released, but we’ll we can talk about that. But please go ahead and ask the

Scott: Yes. So I mean I mean, guess what excites you most about this platform? Have clients sort of been was there a need from clients that precipitated this? Maybe just talk about the sort of origins of this platform and why it’s so exciting.

Rami Fareed, CEO, Schrodinger: Yeah. Yeah. Most definitely. It wasn’t just from our customers, but it was also from our own drug discovery collaborations and our own proprietary programs. In fact, every single project in the whole industry encounters off target toxicity.

That is the molecule that you’re developing in advance and and and trying to improve its affinity to the target that you’re going after. There are always, without exception, other proteins that are either through high sequence homology or just by chance that have similar pocket that the molecule also binds to and that almost always causes some kind of undesired toxicity. So, yes, it’s a very significant need. We’ve known about this for a long time, but the technology just wasn’t wasn’t at at the right state to even think about a project like this. But but as as things go, you know, we kept advancing the the technology.

We kept improving the this this platform of being able to combine physics based methods with machine learning. And we got to a point where we said, wow, we can actually attack this incredibly hard problem. And then through funding from the Gates Foundation, we really ramped up the effort. And what we’ve done so far is we’ve enabled and by enable, I’ll explain what enabled means. But we we now have a virtual toxicity panel of around 50 targets where we can say, give us your molecule, put it into this, you know, send it over to this virtual assay, virtual toxicity, off target toxicity assay, and predict, compute whether the what whether the molecule binds to any of these 50 targets.

And it’s a diverse set of targets. And, we’ve tested this. We’ve actually, started to we published on a few of them, but we’ll be publishing soon. We’ll be releasing that product later this year. And so far, the results are impressive where we can, earlier in a project now, much more efficiently, of course, relative to sending the the molecule out for experimental assays where you actually test it against each of the proteins, we can do it computationally.

And more importantly, and this is really critical, not only do we do we get a sort of, you know, panel of of yes, no’s. Right? You know, you hit this target. You didn’t hit this target. But the ones that we hit, you actually can do something about it.

You know how to because you have a model. You have a you have actually a physics based model that tells you how to essentially, how to fix the molecule, adjust it, change its chemical structure to not hit that target. So there’s a lot of excitement around this, this project, and we think it’s the demand for it’s gonna be pretty significant. And we’re continuing to advance it, obviously, and add more and more targets to it.

Scott: That’s amazing. You said 50, right? You have 50 targets?

Rami Fareed, CEO, Schrodinger: It’s 50, around 50 at the moment. That’s right. Yep.

Scott: While we’re on this page, let’s talk about the next two, I think, more exciting races here maybe on the biologics optimization and protein engineering first. Maybe we’ll start there. Where are we in that development? And what excites you about that?

Rami Fareed, CEO, Schrodinger: Yes. So Biologics design, there are sort of two areas of opportunity to advance the technology. One is in the underlying science. The we’ve developed physics based methods that allow us to predict binding affinity of an antibody to an antigen. And that can be used for affinity maturation.

It can be used also to optimize affinity as a function of pH. We can also compute thermal stability. And so these physics based methods are allowing us to actually design or our customers to design better antibodies. But there’s another challenge, another opportunity, which is managing the enormous amounts of data that get produced by the experimental methods that that produce antibodies. And that requires an informatics platform that can store all that information and deliver it to the decision maker.

People are making decisions about what, you know, what the challenges are with the antibody that they’re working with and how to improve it. And so we’ve extended our small molecule informatics platform to support biologics. And that was released last year, and we’re seeing there’s very significant demand for this because there aren’t any good solutions now. People are actually using things like Excel to do that. That’s crazy.

You know, or their own homegrown things that, of course, are very hard to maintain. So there appears to be a real pent up demand for a way to manage this massive amounts of data that get produced by the experimental method. So that’s it’s called Live Design Biologics. And again, that’s something that that we think is, where that we we think we think that demand is going to result in people really adopting this platform as they’ve done very nicely with the small molecule platform, live design itself.

Scott: Great. Maybe let’s shift over to your pipeline. So I guess, talk walk us through the molecules, the therapeutic areas. Mhmm. I know you kind of did this a little bit initially, but maybe dig deeper when the when the when the clinical readouts and preclinical data comes out for each one.

And really what I guess, Rami, in your opinion, what excites you the most on the internal pipeline side?

Rami Fareed, CEO, Schrodinger: Yeah. Yeah. Yeah. So we’re we’re excited about all of them. And this is the three, and I mentioned them earlier, oncology programs that are all in the clinic where and we’ve said that we will be releasing medical meetings for the first time clinical data.

We’re obviously very excited about SGR1505, our MALT1 inhibitor and B cell malignancies. That’s because that’s the one that’s coming up. That’s the nearest term one in Q2. But, we’re also excited about 02/2021, CDC7 inhibitor for an AML. And we’ll be presenting data later this year in the second half of the year.

And then, of course, fivefifteen are we one MIP1 inhibitor and solid tumors. So again, presenting data at a medical conference later this year. So I know I didn’t pick my favorite our favorite child, but I hope it’s clear there’s reason to be excited about all three of them.

Scott: Yeah. Maybe walk through the competitive environment around the molecule.

Rami Fareed, CEO, Schrodinger: Yeah. Look, I think everybody understands that J and J demonstrated in their release of data that they saw responses and the target is obviously validated, but they also saw toxicity. And that was the thesis from the beginning. And that’s I think you can see that from the platform. Can we design a better molecule with a larger therapeutic window?

We in the preclinical data that we released, we demonstrated that we designed using the platform molecule that’s 50 times more potent. And, of course, in a situation where you have higher potency, obviously, you can in principle, you should be able to dose lower and avoid certain toxicity. That’s something we’re gonna have to demonstrate. And that’s what we’re hoping to show preliminary safety data, PKPD as well and even preliminary efficacy. So, yeah, that’s where we are with regard to fifteen oh five.

Scott: Great. I guess maybe, Rami, the last couple of questions here. How do you it’s more about strategy. How do you think about balancing your internal programs versus collaborative programs? Maybe I’ll start there first.

Rami Fareed, CEO, Schrodinger: No. That’s a really good question. You you can see we have a whole range. Obviously, that even the software licensing, you know, that that’s one part of it. But with regard to the internal versus proprietary sorry, the collaborative versus internal slash proprietary, we like this.

We and we have it this is what we have. We have a balanced model where we’re balancing sort of risk reward profile of the programs, and we really like that. We have some programs where maybe in the form of of of partnerships we have with companies we cofounded where we own some fraction you know, some small fraction, single digit fraction of the company, but the risk is relatively low in those in those cases. And so that’s generating, well, it’s not revenue, it’s generating income every once in a while from the equity stakes we have, and that’s been quite successful. Nimbus, Morphic, Relay, Structure, you know, and so on.

That’s that’s great. Okay. That’s one form. Then we have another type that’s maybe a little bit more risk, but and and a little bit more reward, which is the collaborations with pharma companies where we’re doing a little bit more of the work, where we’re not only doing the the design of the molecules using computation, but we’re doing the chemistry, some of the biology, some of the biochemistry, biophysics, so on. And and so but those generate revenue in the form of upfronts, in the form of milestones.

And in the future, we believe in the form of royalties on sales. And we have quite a few programs where we still we have royalties, on sales. So that’s another sort of form of collaboration. And then, of course, the ultimate where we take on more risk, but of course, the rewards are higher where we own a % of the programs. And again, we like that sort of balance and we can and and we will continue to have that balance.

We think the collaborations are important, not only for the reasons we just gave, which is the revenues generated from but remember, the what we talked about with those combination programs that working with pharma working with the customers is pretty important. So those collaborations are important, But we’re you can tell we’re pretty excited about the opportunities of owning a much larger portion at the moment, a % of programs that we’re we’re taking on ourselves. Yeah.

Scott: Okay. And that leads me to my sort of last question here is we’re almost out of time here. But you’re excited about your internal pipeline, but walk us through the strategy there of monetization optionality, right? So do you do you bring these things to, phase two? When do you I know it’s going to be different for each internal asset and each molecule, but kind of walk us through your strategy and how you think of monetization of your internal pipeline?

Rami Fareed, CEO, Schrodinger: Yeah. So, obviously, it’s very important to be guided by the science and to be guided by what also what’s the best thing for the asset. In a situation where, for example, combinations are critical, that’s pretty challenging for a relatively small company to do on their own, and those programs make make sense to partner. So it is our intent. And I think you can see from the programs that we’ve talked about that these are definitely programs where we’re expecting to see, efficacy in monotherapy.

But but these but the real opportunity is in combination with other, agents that in that situation, it makes sense to partner these programs. And so that’s our intent with, generally with with these programs. Now that doesn’t mean that we will never ever take a program further into phase two. And for the right kind of program, the right profile, right target, the right therapeutic area, that could make sense in the future. But this is our plan with the with these programs is to partner them.

Scott: Yeah. Great. Well, I think that brings us up close to the time here. Thank you so much, Rami, for this fireside chat. Thank you everyone for joining us and kicking off our virtual healthcare conference here.

Rami Fareed, CEO, Schrodinger: Thanks a lot, Scott.

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