Thanks everybody for joining us. Welcome back to the 46th Annual TD Cowen Healthcare conference. It's my pleasure today to be joined on stage by the entire Schrödinger team today, minus Karen.
Yeah.
Who is sorely missed. Maybe we can kinda just go down, Ramy, on my right, the CEO of Schrödinger. On the far right is Richie Jain, the CFO. Then, replacing Karen today.
Nobody can replace Karen.
Yes. Yeah, that's true, very much. Maybe I wanna kinda keep this as interactive as possible here.
I will be checking my phone for any questions that kind of come in, at brendan.smith@tdsecurities.com. You know, maybe I do wanna kinda kick us off with a conversation, Ramy.
Give you a minute, talk about the more substantive update we got last week when it comes to kind of the shifting towards hosted services, ACV reporting relative to excuse me, relative to revenues.
Ultimately what that means and kind of what, all of us in this room should keep in mind.
With regard to the transition to hosted. I think I'll hand it over to Richie in a second, but the most important thing to understand is. Well, there are a few things. One is this is a transition that we started a number of years ago, and it's been pretty successful. We already have roughly a quarter of our revenue is already hosted. The way we deliver the software to the customer or the experience, let's say, even that the customer has, is exactly the same. The price is the same. The way, actually, even the contracts are done. You know, they pay upfront and then they have a license for a year.
It's just an accounting difference where the revenue, if it's hosted, is recognized ratably over the term of the contract versus if it's on-prem or it's recognized mostly in the quarter that it closed. We think this is actually good for customers. We, it's certainly a better experience for customers if the software is hosted. We can support them better. We can see what they're doing, monitor their usage. A lot of our customers are always bumping up against licenses, so that's something now we can see, and we can talk to customers and say, "Hey, you know, you're not utilizing this," or "You're over uti-- you know, you're bumping up against the licenses.
You might wanna think about purchasing more licenses. I hope that, you know, gives the highlights. Richie, anything you wanna add to that?
Just to add, we, you know, we look at the business on a cash flow basis, and this change has 0 impact to cash flow. Because of that, we felt like ACV as an operating metric gives the best sense to investors on how to measure us this year, given that the revenue will have a lot of noise introduced to it because of the accelerated change to hosted. ACV is the metric we went out with, and it's the closest metric to how we actually run the business.
Yeah.
Yeah. Okay. maybe just because you all mentioned this I think a little on the call.
... Just to kinda clarify. The kinda revenue and margin impact into the rest of this year and kind of through into next year, how should we kind of just think about that transition too?
Again, from a cost of goods sold operating expense point of view, there's no change to the dollars. We do expect revenue to decline this year given the most of the business is booked later in the year. As we transition that over to hosted, you just have less days, less weeks, less months to recognize the revenue for this year. This will all, you know, whatever revenue is not recognized this year will be recognized in deferred revenue and will be revenue to be recognized next year. Because of the... but just mathematically because we expect revenue to reduce this year, gross margins, our Adjusted EBITDA will be impacted just numerically for the same reason.
I think it's also important to point out, if it's okay, I just wanna say one thing. This is industry standard. This is a transition that, you know, most software companies are undergoing. This is well understood. The impact, it's temporary, it has on revenue is well understood. A number of companies have gone through this and this is the way, you know, you need to use some kind of metric like ACV to track the growth.
Yeah.
I, you know, We're not doing anything that.
Just to add on that, the more effective we are in converting this year, the lower the revenue will be, which is obviously a little counterintuitive.
Right.
That is.
Right.
The way the math works, and that's actually the way what you want. Given that dynamic, we chose to focus the guidance this year on ACV, because that is, again, truly how we run the business, and it gives you the best long-term view on where the growth is. Given the revenue decline that we expect this year, that is a snapback, and we expect that all to be picked back up in the 2027 reporting year. It's a, it's a multiple year transition, but the first year will be the most off the path you'd expect to see.
Yeah. Okay. Understood. All right. I know a lot of this kinda comes against the backdrop of this transition and kind of the overall business strategy for the company, right?
Maybe let's talk, start at kind of high level, but then how this kind of feeds into the strategy overall, really kind of just the state of the computational platform today. You know, I guess where are you kind of seeing all the changes and evolutions you've made in recent years?
Yeah.
What is kinda drawing new customers to you, and what is kinda keeping customers coming back for more as you-?
Yeah.
You know, undergo this transition now?
Yeah, that's a super exciting thing that's happening now. I don't know if people fully appreciate how extraordinary the advances that have been made in computational chemistry in the ability to actually run a computation, run a calculation that displaces an experiment, that you don't have to run the experiment. That's an amazing thing. We're doing things now that weren't possible even just five years ago. We can, using the physics engine that we've developed, we can predict so accurately key properties of molecules that you don't have to run the experiment. That's an amazing thing to be able to say. We've been doing this for a long time. We've been dreaming about this for a long time, and now it's possible.
The incredible thing about this is that we can run those calculations on a scale that is dramatically higher than anything you can possibly do experimentally. You have the accuracy of experiment, but you have a scale. Let me put some numbers to it. We can in 1 day generate as much data as it would take in 10 years to generate that data if you did it experimentally. What does that mean? That means now you have this extraordinary amount of data, way more than you can possibly produce just using experiment, that you can use to train AI models. AI models are now allowing us to scale the physics to even larger space and allowing us to now explore massive amounts of chemical space. What does that mean?
That means that we are now, for the first time, able to significantly accelerate the time it takes to get to a development candidate and improve the probability of actually getting there. Maybe the most exciting thing is you're doing that now with much higher quality molecules because you've explored such a huge amount of chemical space with very high precision that the probability of success in the clinic is much higher. Those aren't just words. You're hearing a lot of these words from a lot of places, and it's hard to make, you know, figure out the signal from the noise. We've been applying this technology now for a number of years, and the result is quite a number of programs that have actually gone into the clinic. Real programs. This isn't just saying we've solved, you know, drug discovery with AI.
We've produced, 16 clinical assets, a number of them in late-stage clinical assets for which we have royalties and milestones. The newcos that we've, co-founded have had really tremendous success at a, a rate of success that is definitely, you know, better than the industry average. I hope you understand what the technology's doing, how we're able to combine, this physics engine with the scale of AI and actually delivering valuable assets. Now one of the most exciting things now that is happening is, advances in agentic AI that's allowing us to scale this now in a really dramatic way. One of the limitations in applying the technology is experts to be able to use it.
We're pretty excited about advances in, you know, modern, in workflows, but also in agentic AI that will allow us to really scale this platform. I think that's what's bringing all of that. It's what's bringing customers, you know.
Yeah. I think this, I mean, this touches actually on a few conclusions we had from Last night we had a panel with the R&D investments in healthcare AI. We had the heads of R&D from Novartis and Takeda and Relay Therapeutics as well. Fiona from Novartis.
Leveraging this technology.
Exactly, yeah. She gave you guys a great shout-out yesterday too.
Oh, that's great.
Yeah. I mean, I think it gets at some of the disconnect between some of us on the outside who are not using a lot of these tools every day.
We just kind of see pharma pouring more and more money.
What, you know, the rest of us are kind of collectively deeming AI?
Right.
Ultimately what that impact means, you know?
Yeah.
I think fundamentally some folks assume, well, if they're spending more internally on AI, that means that there's less dollars that they're willing to spend externally, rather than what it seems to be, that they're now identifying where the holes in their data are and who they need to kind of turn to help plug those holes and train the models better, right?
Exactly. I think this is well understood by the experts, you know? It might not be so well understood by investors and, you know, the general media, but it's very well understood that these pure AI models that are trained solely on experimental data are very limiting. Because by definition, drug discovery is about finding new chemical matter, right? Novel IP, and that means that predictions of those molecules will not work because they're not in the training set. You need physics, you need first principles methods to build those training sets. As I said before, you can do that now thanks to advances not only in the physics-based methods that we've developed, but huge advances thanks to NVIDIA in hardware and then of course, you know, the ability to scale all of this with AI.
Yeah, pretty exciting.
I mean, okay. We talked kind of a little bit about the actual transition in reporting for the software business.
You know, all things else considered, how should we think about maybe the next 12, 18 months, any kind of important inflection points in the actual growth of the software business itself? We know the predictive toxicology offering is now in beta testing.
Yes.
And, and widely-
It's yeah, released.
Yeah. as we kind of think about.
Yeah.
You know, your assumptions for guidance within the context of ACVR revenues, but realistically just actual growth of the business over let's say the next 18 months.
Yeah. First of all, predictive toxicology is a really big deal. This is one of the great challenges in drug discovery. It's very common for a drug discovery program to run for five years and you do all of this work and spend all this money to identify a development candidate, then you start going into tox studies and you discover that there's a tox problem, that's the end of it. That's it. You don't know how to fix it. You have no idea where the toxicity... It's a major source of failure. This is a big deal. We have launched it. It's come out of beta because the beta feedback was really incredibly positive.
There's a huge amount of excitement, obviously, in solving this grand challenge problem in drug discovery. We expect to see growth from predictive toxicology this year and into the future. There's a lot more work to be done. There are, in principle, 20,000 proteins that you don't wanna bind to, right? The proteins in the human genome, and there are some estimates that it may be a bigger number. We're at roughly 60, 70 off-targets that we've enabled in this predictive toxicology panel. We're adding more as we go. That's gonna be a big area of research and of growth. Now, the other thing is what we were talking about before.
Not every pharma company. You've met a few of them that are using the technology at scale to de-generate these training sets for AI, not every pharma company is doing that yet. They're using it. All of them are using it, they're not using it at full scale. We're expecting that to change. We think, there have been enough companies that have transitioned to the sorta large scale use of these methods, that it's sorta de-risked, now it's just a matter of more companies sort of adopting the technology at that scale. That's another major source of growth this year in the timeframe that you said and beyond. Just the actual continued scale-up of the usage. The other area that a couple areas we're excited about too is biologics.
Our platform has largely been developed and validated in small molecules, but obviously biologics are very important. We've been putting a lot of work into that, both on the informatics side, but also on the physics side, and there have been a number of advances there that we expect to be able to contribute to growth. If I can just touch, I know this is a healthcare conference, but I'll just say just very briefly, these physics-based methods can apply to other systems as well because physics is physics. So we're pretty excited about the work that we're doing in battery chemistry. Again, I won't spend a lot of time on this, but there are very interesting material science applications in pure material science workflows like in design of batteries. In pharma, formulations is a material science problem.
We have new products in that space as well, in particular crystal structure prediction, which is an incredibly important part of formulation and drug discovery that we also expect to contribute to growth this year. Pat, did you wanna add? Is there anything else? Did I cover everything?
Yeah, a lot of it. I mean, I think one place that we're investing in too is beyond just the physics simulation, you know, we've had the LiveDesign platform-
Yeah.
... Novartis talked about using for, to own data. It's really important to be the central platform of drug discovery that allows you to... Anyone who's building any AI models, you want them using it through your platform. You know, then you become that central hub, which we've successfully done for the strong majority of pharma in the small molecule space. We have introduced now a large molecule offering. It's especially tempting for people in ADCs and peptides, which are a little hot these days. But because both small molecule and large molecule technologies work for or might be necessary for these, we're uniquely fitted that we understand both of these very well. Most software companies don't. We're very excited about our LiveDesign for Biologics application. We think that's a great growth opportunity.
Yeah.
Brendan, I'll just add, we are, we're an R&D company. We invest in R&D for our customers who are doing high science R&D. The investment is to expand our addressable markets. Within life sciences, where we've existed, predominantly, a lot of the new products we're introducing are immediately adjacent to our customers today, but touch new budgets, touch new capabilities. As Rami mentioned, in material science, there are endless end markets there. We're really excited about the opportunity. We don't typically talk about new products before they're released. Predictive toxicology was an exception to that, just given the amount of industry attention on it and the FDA mandate around New Approach Methodologies.
Some of the other products that we've rolled out on our call last Wednesday are the way we typically release, which is we develop the product and then we launch it to customers.
I'm glad you bring up the NAMs, right? It's kind of next natural question that comes out of this a lot. Maybe just help us understand, like, where this predictive toxicology offering is actually situated within the...
Yeah
... FDA initiative, right? Then maybe-
Yeah.
In that same breath, you know, when I guess I should first ask, have you gotten new customer inbounds or, you know, from existing customers specifically tied to some of those initiatives, and when we should realistically think of the impact of that...
Yeah.
To software growth?
At the moment, of course, the computational methods that have been developed for predicting toxicity are wanting. They're not very good. There's a very heavy reliance on doing it experimentally, which is time-consuming and expensive. What does that mean? It means it gets done pretty late in the process, as I was alluding to earlier. Then it's just, that's it. Program's dead. You just lost, you know, a huge number of years, and that's a real issue. The methods that we've developed, again, as I said really earlier, are highly accurate, really predictive. They predict whether a molecule will bind to one of these off-targets, so-called off-targets that are associated with toxicity.
What that means, of course, 'cause it's a lot faster and a lot cheaper to do it computationally than experimentally, it can be moved up really upstream, really early in projects. In other words, it becomes part of the multi-parameter optimization of a molecule. You do it really early on, and you make sure that by the time you get to the end, when you get to a development candidate, you've addressed, not only affinity and solubility and permeability and so on, but you've also addressed selectivity and therefore, toxicity. I think there are two applications. One is new. It's completely new market, right? People using this early in discovery.
Of course, it's still really valuable in the later stages when you're starting to think about what molecule to put into animal studies, and that's where it ties into the FDA. Now, the FDA is saying they want to eliminate animal testing. I think every time somebody hears that word, they think, "Come on, that's crazy." It's okay. It's okay to think crazy because, you know, the future isn't so far away. It isn't gonna eliminate animal testing, but it's clear, and it has been reducing it because, of course, if you have a molecule that's lighting up in this computational assay and saying it's going to be toxic, why would you put it into an animal? It will certainly result in reducing animal testing. Maybe in the future, it'll eliminate it.
That seems really far-fetched that you would actually use humans to test the toxicity. It's okay. You get the idea. It's gonna significantly reduce it.
Yeah.
I think those are the two applications.
One other thing I'd elucidate is it's not just for animals. We can test both the animal and the human protein. You might go through the entire development process just looking at the human protein, and then it fails in animals, and you're like, "What just happened?" Since you can test both of those, you'll be able to uniquely identify ahead of time. If you see something in animals that's different than what you're seeing in humans, you might be able to know ahead of time and expect that. Or vice versa, you know, the worst-case scenario is it looks fine in animals, and then it has a problem in humans. Knowing about that earlier, obviously, is incredibly valuable 'cause clinical trials are even more expensive than the animal studies.
That's extra knowledge that just, you know, largely doesn't exist right now. There are some experiments that try to get at it, but that level of information and being able to push that early, should dramatically increase the success.
Got it. Okay. When we kinda think about now, continued evolution of the platform.
Promise you I'll continue to come back to this question over the months and quarters ahead.
Good.
Now that you're all kind of transitioning really to a fully fledged software entity with
Yeah.
With a few notable exceptions around the
Yeah.
Around the edges there, how do you kinda think about continued evolution of this, right? Obviously, we have the predictive toxicology, but you mentioned biologics, you mentioned a few other modalities?
Yeah.
Is it kind of order of operations to expand within what you've got to other modalities and then maybe to other parts of drug development spectrum? Like, where does that kind of strategy fall?
As Richie said, we're an R&D company. We have a significant investment in the platform. One of the areas that we're super excited about right now, it's actually what I'm about to describe is a technology that's actually enabling the predictive toxicology initiative, and that's, in general, just protein structure prediction. There have been a lot of advances in computational methods and experimental methods for determining the structures of proteins. You heard about it, AlphaFold. I mean, there's a Nobel Prize, right? What's not so well known is that the output of those of those initiatives, the experimental and the computational, is pretty low-resolution structures. They're not actually that useful out of the box. We are developing methods for refining those structures to high resolution, which is actually what you need to make use of them.
You gotta get the details right. If you hear me say the word physics, right, physics-based method, you can imagine the input to a physics-based method is getting the positions of the atoms in the right place as a starting point. That's pretty important. We're putting a huge effort into determining the structures of proteins and the molecules that they're bound to to high resolution. Now, what does that do? At the moment, we really only know the structures of proteins, human proteins, to high resolution of maybe 10%, 15% of the human proteome.
Obviously, the ability to, you know, to scale that up to 100% allows us to work on targets that we otherwise can't work on, the so-called hard to drug targets that are implicated in, from the point of view of biology, in important diseases, but we just don't know how to target them. Is it with a small molecule? Is it a peptide? Is it a degrader? You know, if it's a small molecule, is it a macrocycle? Is it small? Is it big? Is it? You know, right? All that sort of thing. Enabling us to actually explore all of biology, through, knowledge of the structure is huge to really open up the...
Again, that's the technology that's being used to enable us to be able to predict binding to off-targets, but obviously find, you know, identifying or, being able to, design molecules for targets of interest from the point of view of, you know, solving diseases, is obviously a really big, is important and a big area of research.
Yeah.
So-
I just wanted to add on, too. We have the ability to see our most popular workflows that are used in our software, and by far our most popular workflow is one that takes PDB structures and cleans them up because your average PDB structure is so far away from being usable in drug discovery. I think this is really important because most of these AI models are trained to try to reproduce the exact PDB structure, which our customers are telling us through that utilization are not good enough. The best-case scenario is they're reproducing at the same quality that is not good enough for use, and that's why we really invest in that.
PDB structures, that's the structures in the public domain.
Yeah.
I guess kind of tied to this now, before we get into the therapeutics pipeline, just in the last couple minutes, I did want to ask the partnership strategy. I know you all kind of announced a new partnership with TuneLab over at Lilly. And I think that's kind of more focused on this idea of kind of federated learning, right? So, but maybe help us understand how that approach and that specific partnership relative to like the Novartis, right, that we've talked about before, how does this all now fit into Schrödinger's overall partnership strategy and where that kind of fits into the growth story for the software business overall?
I mean, yeah, we're super excited about TuneLab. TuneLab covers a huge gap that biotechs have. For as long as companies have had LiveDesign, which is approaching 15 years now, which is kind of crazy, every big pharma has put in these machine learning trained tox models built on all of their data. They've gotten a little better at it, but really it's just kind of around the margins. One limiting thing is how much data they have. What we see happening is when people leave pharma and they've gotten used to doing drug discovery with these ML tox models that they've built, they go to found a biotech and there's nothing. There's no public because this is all built on their internal things.
What Lilly's done that's awesome here is they've figured out how to give every biotech out there access to those types of models. Obviously Lilly's not just doing it for fun. The biotechs then put their data back in and Lilly's own models get better. It's super exciting for us because selling LiveDesign on this entire time, the first question is, "Do you have any suggestion for how I get this model like I had back at Big Pharma X?" Our answer has historically been no. Now, I do want to address the elephant in the room because we get a lot of questions. Isn't this directly competitive with predictive toxicology? It's not. The accuracy of these type of models is totally different. They're very useful.
They're often on endpoints that are much higher level than what we simulate, the correlation between the endpoints is much lower. It is still useful, obviously, but when they're using our engines, they typically expect an accuracy that they can make confident decisions in. These are more kind of like red light, green light hinting accuracy. Still very useful. I don't mean to denigrate it at all. It's just a different tier.
I just want to add to your kind of long-term growth strategies and how we partner. What we see as a long-term driver is the enabling our users to become power users. We have throughput-based prices, throughput-based licensing and pricing. The more any individual user uses, we are able to capture that value. As the workflows become more efficient, we can enable our users to be able to run more and also expand the amount of users who can run our technology. Pat is actually working on a lot of the integration with LLMs and other agentic AI processes that will be able to expand our user base over the years.
Okay, great. I think now in the last few minutes here, I did want to touch a little bit on kind of the status of the therapeutics pipeline, right, both internally and partnered here. Maybe just quickly give us a sense of, you know, when we could get updates from, you know, SGR-3515 and SGR-1505. That's the Wee1/Myt1 and then MALT1 inhibitor, respectively. And ultimately kind of, you know, what the status is of both assets, whether that's, you know, external exit licensing and partnership discussions, where that all stands now.
Sure, I'll address that. Our intention is to finish the dose escalation studies on both 1505 and 3515. We've presented data on 1505 last year. 3515, we should be presenting data in the second quarter of this year. Importantly, we've announced that we see the best way to advance these assets are with partners in the mid and late-stage development. That's where the focus is. We'll give updates as we have them.
More broadly, as we think about therapeutics, we continue to be really excited about the collaborations portfolio, working hand in hand with our pharma partners and not only generating IP and delivering development candidates, but enabling broader adoption of our software within those customers and also generating downstream milestones and royalties that are, you know, we're, that we're accruing at this point. We're excited about the targets and the indications and the royalty rates that we have there. We gave some additional disclosure in our results last week to kind of give a sense for what that opportunity is. You know, five of these programs are in $5 billion-plus markets where we have royalties ranging from high single-digit to low double-digit ranges.
It's fair to assume now kind of in perpetuity moving forward, at least for the foreseeable future, that, you know, any new therapeutics, new drugs that could come out of the Schrödinger platform would largely be relegated to existing partnerships that you have with pharma and biotech externally, right?
That's fair.
Okay.
That's right.
All right. I guess just in the last minute here now, I want to kind of pull everything home a little bit. We've talked about, you know, the transition with ACV. We've talked about.
-Therapeutics pipeline. We've talked about partnership strategy and evolution of the platform. You know, with all of that said now, as you kind of look ahead not just into the latter half of this year, but really into kind of this next era for Schrödinger, where is kind of the biggest disconnect when you talk to folks who are trying to understand where you all fit in, what the real growth drivers here are, and ultimately where they are kind of trying to value the platform?
Yeah. I think the biggest disconnect, and you can't blame people for this, is there's a lot of noise out there. I mentioned it earlier, right? There are an uncountable number of companies labeling themselves as AI companies that have completely changed drug discovery, have solved the problem, and they're publishing blogs and publishing white papers and doing comparisons and getting a lot of attention, actually. It must be really overwhelming for... I mean, how are you supposed to do, unless you're an expert in all, in physics, chemistry, biology, and computer science to make, you know, to figure out the signal from the noise? What I would invite people to do is just look at the track record.
That's important, right? What do these companies actually produce? What have we produced? I think if you do that, there's a very clear distinction between companies that are just saying that they're doing things and running retrospective analysis, right? Running calculations on things that are already in the literature and saying, "Look, it matches up." As everybody knows who's developing machine learning and AI that's really easy to do 'cause those things are in the training set. That doesn't count. If you haven't produced development candidates and clinical assets, and you don't have 100% customer retention, you know, from customers, I'm not sure you should be out there, you know, doing all of that.
Sorry, I know that sounds a little bit critical, but you can imagine the frustration from a company that has been doing that for a long time, has had a track record of delivering over and over again high-quality clinical assets that are progressing through the clinic, for which there are very meaningful milestones and royalties, by the way, on quite a number of them. The success of the companies that we've co-founded is striking. I mean, it's noticeable. That's not normal, you know, for that, you know, that number the sort of success rate.
Success rate, yeah.
Then the size of these, you know, exits and so on is really meaningful. I hope I keep saying it and having an opportunity in venues like this to highlight it. I would encourage people, go look at it. Look at the pipeline. Look at the success. I hope you will see a difference. Don't take our word for it that we're just saying we have a physics engine that's accurate. I mean, look at the results. I think that's really important. I think the other thing is, don't be... It's very dangerous when there's a new technology. It's so easy for it to get over-hyped.
You, you kind of, you have this tendency to sort of extrapolate into the future and say, "Well, I don't understand this stuff, but boy, it sure looks like it's gonna be able to do something that is magical, and I don't really understand." Usually, that isn't the case, right? It's, it's a technology like everything else. It has a domain of applicability. It works in certain circumstances. It doesn't work in others. You know, treat it like a technology, not like magic and some.
I hope it's useful.
Yeah.
Looking at the actual use cases.
Yeah.
... For a lot of the tech as it stands.
Yeah.
All right. Well, thank you guys for joining. It's always a pleasure to see you. Thank you everybody for listening. We've got more to come.
Great.
Thanks.
Thanks. Thank you very much. Thank you.