Great. Thanks so much,
Oh, geez.
To everybody joining us here. Second day of Piper Sandler's annual healthcare conference. I'm Joe Catanzaro, one of the biotech analysts. It's my pleasure to welcome the team from Schrödinger. We have the full team here: CEO and President Ramy Farid, President of R&D Therapeutics, Karen Akinsanya, and CFO, Geoff Porges. Thanks so much for joining us. Maybe before we jump into questions, Ramy, I could give you a few minutes. You could give a quick introduction of Schrödinger, what you guys have been up to, and what we have to look forward to.
Sure, sure. Be happy to. So, yeah, just as a general introduction, we've over the past 34 years since the company was founded have developed a computational platform that is designed to mimic essentially the synthesis of molecules and the assay-assaying of those molecules on a computer with the goal of essentially doing as much of drug discovery computationally. You can imagine the incredible benefit of doing that, as you can actually synthesize molecules on a computer and test whether they have drug-like properties.
You can imagine the scale that that can be done on and the ability to identify molecules with the appropriate properties by actually exploring huge amounts of chemical space. That results in increasing the probability of actually identifying a molecule with the appropriate properties. As we've shown, it's also leading to development of or discovery of very high-quality molecules.
Now, what we do with that platform is we license it to all the pharma companies. So, all the top 20 pharma companies are using the software to advance their own programs. We have thousands of customers in addition that are using the software in both life sciences and material science, so molecular design and all types of all sorts of modalities. Now, we also use that software to in collaboration with pharma companies and biotech companies to advance programs together collaboratively.
Some of those biotech companies we actually co-founded. We also collaborate with a number of pharma companies. We just announced a pretty big collaboration with Novartis. Maybe we'll talk a little bit about that. We also are advancing our own pipeline of wholly-owned programs, three of which are in phase one clinical trials. Maybe we'll talk a little bit about that today. And a number behind that that are advancing through discovery.
Great. That's perfect. And so maybe we could start on the software business and sort of start really high level on what you guys see as the key sort of medium-long-term drivers.
Yeah.
That is gonna allow you to continue to grow that business.
Yeah, yeah. So what we have discovered, and it's a good thing we're using our software, we have a very good idea about what it takes to actually advance programs, to do all the things we just talked about, to be able to advance a drug discovery program more rapidly, to identify high-quality molecules. We have a sense about what scale of usage of the software is required to achieve these sort of pretty remarkable outcomes that we've had, again, in these collaborative programs and our own internal programs.
Now you can ask the question, well, what scale are pharma companies using the software at? We're very happy to report that they're all using it. You know, again, there are thousands of customers. But are they using it at the appropriate scale?
The good news is a few of them are, actually, but not all of them. That's obviously a tremendous opportunity for growth. We see that's a significant source of growth of the business: pharma companies going from where they're at right now, which is good. It's not zero, but scaling up to a usage that's approaching the usage of the software that we're, you know, we use internally. That's a pretty significant source.
Maybe on that point, we could. You mentioned the Novartis deal.
Mm-hmm.
Maybe you could speak to how emblematic you think that is of where large pharma is going in terms of scale of utilization.
Yeah.
Also, the way the deal is structured, there's a lot of collaboration between you and Novartis and sort of, you know, making sure they are using the software to its full sort of utilization.
Yeah, yeah. Maybe that's something Karen can.
Yeah, so I think what's interesting about the Novartis collaboration is that, of course, they've been using a variety of different tools. They even have an internal team who have been for years trying to kind of create sort of computational methods and sort of trying to install that across their groups. What I think this deal sort of is symbolic of is the idea that there is a professional solution out there.
And it's better to just embrace that and deploy it across their entire network of drug discovery teams across the US and Europe. That's one thing because, once you have that systematic deployment, then there's the opportunity for the growth that Ramy talked about. As they get used to it, we're gonna be helping them with that. So the drug discovery team, we're gonna be working collaboratively on a set of programs.
So they'll see very, very, in real time how it works and how to get the most out of it. And then they will deploy that over time, on all of the rest of their programs. We don't know how many they have, but obviously they have a lot of small molecule programs. And so, that's essentially this sort of synergistic, sort of two-part deal that was done.
Any sense of, what internally at Novartis occurred that led them to sort of make this decision to make a bigger push and whether you could try and replicate that at other pharmas who aren't utilizing at scale?
Yeah. I mean, I think one important piece is, the head of R&D, a lot of knowledge, of the platform from previous experiences. So I think, you know, Fiona Marshall was at Heptares and then at Merck and, just a lot of interest in transforming the way drug discovery is done. And so completely agree that the opportunity to kind of replicate that where a head of R&D is now embracing computation as a way to transform and improve the R&D ROI, actually, the idea that we could do things more efficiently. We see that as an opportunity across a lot of the large pharma as you get new heads of R&D coming in who are really looking for that way to introduce computation, ML, but also to improve efficiency of their teams.
We can't, we shouldn't forget that at the heart of the collaboration were some inventions that we had internally.
Oh, yes.
Some programs of our own. So maybe you should talk about that because that was a cornerstone of the whole thing as well.
Yes. I think indeed, typically when we're, so the drug discovery team within Schrödinger has two sort of roles, one of which, of course, is developing a pipeline. But we also, as Ramy was pointing out, are great ambassadors for how to use the platform. And so we were actually demonstrating the programs that we're working on across a range of areas. And they got very excited about those programs, and decided that, wow, we should collaborate, join forces, so they actually took a license to several of our programs. And that allowed us, essentially to accelerate our own efforts on those programs. So I think it was a win-win, actually.
Is there enough still left in that early pipeline to structure a deal similar to Novartis where you know you sort of do offer programs early in your pipeline to pharma that you've sort of identified and worked towards?
Yes. There's actually a team dedicated to the idea that, of course, we can't do drug discovery for the entire industry. But what we can do is understand the cohort of programs that are very well suited for our platform but are also exciting from a therapeutics development point of view. And so I think increasingly, we haven't completed the sort of full landscape of targets that we would say are substrate for more BD deals. But we do think this is an important strategy, to have a set of targets that are good substrate for business development discussions.
Another piece of recent news from the team was the expansion of your predictive toxicology efforts.
Yeah.
Maybe you could speak to sort of what triggered that expansion, what the additional funding will allow you to do?
Yeah.
When you'll be in a position to potentially offer that to customers.
Absolutely. So let me first describe it. So this is an effort at structural enabling off-targets that are often responsible for safety profile of a molecule. And what that means is when it's structurally enabled, in other words, when you know the structure of the protein, you can use the physics-based methods that we've developed to actually predict whether a molecule binds to one of these proteins. Now, how many of those proteins are there depends on how you think about it. I mean, I guess there could be thousands, right? But they're on the order of hundreds that are sort of well characterized and.
Yeah.
Often come up and are a primary source of failure of programs once they get further along, including even in the clinic. So the original goal was to identify 100 targets and develop essentially a tox panel for those 100 sort of well-known, you know, off-targets. And we said that would take roughly three years. The additional funding, which is sort of, you know, an add-on to the grant, is simply gonna allow us to accelerate that effort.
And we've already seen that happening. So we said this when we started that we had enabled five of the targets. And you can almost imagine what they are. I mean, they're the five that keep coming up over and over again, hERG, you know, P450, PXR, you know, so. But there are obviously many, many more. There are 100 that we identified. So we've already, you know, we've been able to accelerate and get more and more and so expand that panel and essentially accelerate even the release of it as an actual product. We're, of course, using it internally already. But obviously the plan is to eventually license it.
Maybe more generally the idea of offering new product offerings to partners. How important is that to the growth of the business? Is that an incremental contributor or is that gonna be an important contributor?
We have a long track record of very significant sort of breakthroughs in technology. And every time we do that, it results in a pretty significant inflection in the software business. We think that this is in that category. It's a basic science project. So it's hard to predict for sure. But it's in the category of a number of other initiatives that we've had like this, multi-year projects that we think at the end of it, something extremely compelling comes out of it. And if that's the case, we think it is. I mean, it certainly has the potential to do that. This is obviously an incredibly important product, yeah, exactly. I mean, you know, technology, it has the potential to have very significant impact on the software revenue.
So, we're at near year-end, pretty important time, I guess, for you guys and contract renewals.
Mm-hmm.
Maybe, Geoff, you could sort of speak to where things stand, what the outlook looks like, what you're hearing, what you're seeing.
Yeah. We, as you know, we raised our guidance for the year, or at least now at the range, towards the upper end of the previous range. And so we've got it to 8%-13%, which implies a big chunk of revenue in the fourth quarter. Our business is always seasonal towards the fourth quarter. It's not truly seasonal, but that's just when all the renewals occur. And gradually we're shifting the renewals over to a more ratable basis.
So that fourth quarter dependence will gradually go down, but it's not gonna go down in the near term. So, we're having very, very productive discussions with our large customers. I would say, there's a, it's almost like two different worlds. The conversations with the large customers are very constructive and, and I would say, you know, consistent with our guidance.
There's still a lot of noise in the small customer landscape. We're still seeing new companies come along and say, "We really we've just received venture funding. We wanna go after this target or this disease, and we'd like to use your platform. Can you help us with a creative financing strategy?" Sometimes, we're seeing plenty of those discussions. But equally, we're seeing still companies that say, "We're not doing drug discovery anymore." So, so that kind of noise in the biotech landscape is still there. But we're really not hearing anything, related to that in the large company renewals.
In fact, probably at, in the other direction, you know, the same sort of thing that Karen described with Novartis, where we're seeing really a sea change in the understanding of the potential of computation in the C-suite that's trickling down company by company to opportunities for us to grow the business. So we're confident about this year and also see those opportunities coming forward for several years in the future.
For those large company customers that are thinking about scaling up, what are the key questions they're asking?
Yeah. I think Karen touched on that, actually. It isn't anymore, does the platform work? What's the validation? I mean, that's overwhelming now. There's no issue with that. It's do we internally have the expertise to actually deploy it? We they know they need to be deploying it at scale. So they're asking questions about how can you help us? And I think that's what Karen addressed. And that's, that's a very effective way of doing that is by collaborating on some programs together.
So, maybe last question on sort of the software business. I think something I asked last year, something I'm asked every so often about the competitive landscape in sort of physics-based drug discovery.
Yeah.
What's the outlook like? Where's your positioning? How do you see it maybe five years into the future?
Yeah. So we have a very significant lead right now. And there's a rather large moat between, you know, where we are and other efforts which are quite small compared to the effort at Schrödinger, where the leader in this space. We develop these methods. We were the first to develop them. We're continuing to advance them. We have a very significant effort in this area. I mean, roughly a third of the company is devoted to advancing the platform. That's a few hundred people, so we're not standing still. There are other companies, mostly academic groups. And so some of the companies are working with academic groups. So the efforts are much smaller. And they may have a little pieces of it here and there.
As far as we can see, there isn't anybody that has any. We don't have sort of traditional competitor in the sense that there's a software company that sort of, you know, has an alternative that is a viable, well, a viable alternative to Schrödinger technology. So when we don't, that's not what the challenge is in scaling up. It's what we talked about before. The competition is really doing drug discovery in a traditional way. It's not using other software.
Now, I think if we stopped and we said, you know, we're not gonna develop the platform and we can become sort of complacent, I mean, this happens in every industry. Eventually, you know, you have to accept that other companies will catch up. So I think as long as we continue to advance the technology, the predictive tox is a good example, I think we can maintain that very significant, competitive advantage that we, that we have now.
I wanna ask one question quickly on drug discovery as I think back to full year 2024 guidance early last year and sort of maybe the disconnect that was there between guidance and consensus as we look to full year 2025. How should we think about that? How does the Novartis deal sort of help you? How does the additional funding on the predictive toxicology help you? Maybe you could just sort of frame that up.
Yeah. You're right, Joe. There was that disconnect. And the reason that the outlook, I think, was not consistent with those expectations was partly because a number of our collaboration partners made decisions about their portfolio. They basically shrank their portfolio of drug discovery programs, and that affected our outlook.
We are in much better shape this year looking to next year, because we have the Novartis collaboration and we announced there's a $150 million upfront payment and that we will recognize that revenue over the period of execution of our obligations under the agreement. And that's, you know, roughly four years. You can't do the math. There's gonna be a chunk of revenue that's associated with the work that we do next year that will contribute to drug discovery.
There are, of course, other programs that we're continuing to advance with other collaboration partners even beyond Novartis. We're feeling pretty good about it. We did comment in our earnings call that we expected the drug discovery revenue to be higher next year than this year. We're not sort of firming up to any sort of range of numbers. In part, it depends on how the projects go and how quickly we deploy the teams and scale up on the activity.
The other thing I'll say is we are not standing still. We have a great collaboration in place with Novartis. We have existing collaborations, but we have plenty of opportunities for both continued partnering discussions and for further collaboration. That's also an opportunity for us to drive revenue on the drug discovery side.
Great. Maybe in these last few minutes here, we could shift to the proprietary pipeline and just start with sort of the general thinking to shift more resources and allocate more funding towards the proprietary pipeline and sort of what drove that decision, and maybe we could dive into some of the specific programs.
I mean, I think if you go back five years, we obviously already had a very successful drug discovery business, let's call it, with the collaborations with Nimbus and Morphic and other companies, but I think it was pretty clear that while we were obviously generating value for the company and Nimbus's payout last year was pretty significant in the $100-something million, right?
We obviously owned a smaller portion of those business opportunities and recognized that we had enough expertise internally and growing expertise internally to be able to run our own drug discovery programs, and actually now we have a small, lean but very effective clinical team who are running three global clinical trials. Now, I wanna go back to Novartis for a minute. We're not saying that we have to take all the programs into the clinic to realize value.
As Jeff just said, we got $150 million for some programs that we put out into that collaboration. So, we think that we can generate value from doing drug discovery internally in addition to the equity stakes that we have in smaller companies, but the decision to move programs further is really about getting to inflection points where we think the value is optimal, and so the first three programs, we have MALT1 inhibitor for B-cell malignancies. It's in a phase one dose escalation, CDC7 in AML, and our third program, WEE1MIT1, also now in phase one. We believe that being able to partner those programs at this early clinical stage is going to be another form of value creation for the company.
So I maybe wanna come back to that last point, but maybe first you could talk about sort of target selection. Maybe we could use 1505 as an example, MALT1. What drew you to it? And where do you see the opportunity for 1505 to differentiate itself from some of the competitors?
Yes, absolutely. So one of the important things that we think about as a company that is really a chemistry company is we are not about to take enormous biological risk and work on, you know, just highly speculative biology, which frankly is a mainstay of biotech because there's high risk, high reward there. But that's not the model that we pursued. We actually want to anchor our program choices in human validation. And that means either genetic validation in the case of 1505. It's a MALT1 inhibitor. It's in the BTK pathway. Upstream and downstream of MALT1 are clinically and commercially validated targets. However, as you know, that in lymphoma, a lot of people relapse on BTK inhibitors.
The idea that you could inhibit MALT1 and combine that with a BTK inhibitor or a BCL-2 inhibitor, we saw that this had been shown, but there weren't great molecules for it. Even though it was a sort of program that wasn't in the clinic at the time, very quickly, MALT1 was semi-validated by the fact that the big BTK franchise owners, AbbVie and J&J, were also in the clinic. We believe it's an important target in the space. We also are very excited to continue working up our data to show that we think it has a great profile relative to the competition.
So we'll get an initial look at the data in first half 2025. What are you hoping to be able to say about the program?
Yeah. So I think one of the things you raised is how did the molecule perform in the clinic? What was the PK like? What was the safety like? Did it hit the target in the way that we anticipated? And do you begin to see signs of efficacy? Obviously this is a phase one dose escalation study. It's not powered for a full-on efficacy readout, but we are obviously very interested in being able to see and share signs of clinical activity.
Great. So you mentioned PKMYT1, WEE1. I think I'm the analyst that covers the most PKMYT1, WEE1. If you could believe that.
It's a claim to fame.
Yeah. Where do you and we've seen a couple of data sets. Where do you see the sort of best opportunity to try and differentiate yourself versus those?
I think that, first of all, the notion that WEE1, which is really a validated target at this point, it clearly works in uterine serous carcinoma and other gynecological cancers. I think the issue has been around that therapeutic index, being able to thread the needle around hematological tox. We've actually established with our molecule that you can, because of this WEE1-PKMYT1 synergy, you can dose for a very limited period of time, get the same, at least in the preclinical models, the same kind of efficacy that you see with continuous dosing, but then give the heme compartment a break.
And we think we've optimized that profile, certainly in that single molecule, 'cause you know, there's a lot of WEE1-PKMYT1 combinations of two novel molecules. We have a single molecule that does that. And we think the behavior of the molecule will combine well with other agents. So we're looking forward to seeing our data on that program later on next year.
I wanna come back to your comment, Karen, on sort of partnering the proprietary portfolio. I sort of read your comment as get the proof of concept and then look to partner. Maybe you could sort of elaborate. Maybe I'm misinterpreting that sort of the bigger picture strategy on sort of where you take these programs to. Is it proof of concept? Is it phase two when you look to sort of find strategic partners?
I mean, I think it will always, always be data-driven. However, we can say that one of our strategies was to pick targets that are in precedented pathways. We talked about that for MALT1, which by default means that a combination play that there is a big commercial franchise out there that frankly we don't have, so if you think about BCL-2 inhibitors and degraders and you think about BTK inhibitors and degraders, we think the best way to develop those after you've demonstrated either proof of concept or end of phase one is to work with these large franchise owners.
Now that doesn't mean, for example, in our modality switch effort where we're working in I&I, where we believe that these could be extremely exciting, programs for us to take into the clinic and potentially, take into narrow indications where we would have an opportunity to realize more value, we maintain that flexibility. It's not our primary strategy, but we like the idea that there's flexibility to continue building value in some of these programs.
Great. Perfect. Well, with that, I wanna thank Ramy, Karen, Joe for your time and thoughts. Enjoy the discussion. Thanks everybody for joining. Take care and enjoy the rest of your day.
Thank you. Thank you.