Recursion Pharmaceuticals, Inc. (RXRX) Earnings Call Transcript

All right. Good morning, again, for I see some familiar face in the last one, but thanks for sticking around. It's good to see everybody, and welcome back to TD Cowen's 46th Annual Healthcare Conference. It is my pleasure to be joined today by Recursion's CFO, Ben Taylor. We've got a lot of great stuff to unpack, discuss, sift through. So we have our own questions, but by any means -- by all means, excuse me, if you've got any questions, just kind of flag me or feel free to send me an e-mail at brendan.smith@tdsecurities.com, I'll be checking my phone.

But Ben, maybe let's just kind of start by taking a quick second to update us on really the Recursion platform of March 2026. Where do you kind of see the most important areas of evolution recently? And what this kind of tells the rest of us about what the platform is actually able to do today that maybe it hasn't been in years past?

Sure, of course. Well, and I think there are a couple of different pieces that come together. We have obviously changed the platform and the business model quite a bit over the last 18 months. And what I think that really reflects is going from a single point solution focused company or companies to really trying to create an integrated platform, as well as an integrated business model. And so what I mean by that, maybe starting with the platform first, Recursion, a couple of years ago was really focused on inductive phenotypic screening to uncover novel biological targets. And really, how do you translate that novel discovery into a clinical program and in the future in medicine. What you've seen now in our platform is we've taken that novel biology, and we've added on transcriptomics to it. We've added on proteomics to it. We do a lot of reverse genomics using real-world evidence. But then we've also added in through where I came from on the Exscientia side, a novel design platform that's really looking at how do I take that nearly infinite chemical space and try and come up with a good chemistry to try and test that biological thesis and try and solve problems where the industry hasn't solved it before. And then we added on to that as well, and this was really thanks to our new CEO, Najat Khan, who had joined from J&J after overseeing their portfolio strategy as well as developing a lot of their AI technologies. how do you look at the clinical side of the world, the real-world evidence, how do you design better trials, but also using that to bring back into the biology and chemistry and thinking through what's the right patient and how should I design this for the right situation. And so what you've seen is we've come together. And that's become a single workflow across the company where we're saying, what can I use to better understand the biology, the patient, the chemistry, the clinical trial I need to run, so that I'm incorporating better predictive modeling so that I hopefully have a lower failure rate in the clinic. And so that's been how the technology platform has come together. At that same time, we've also been really trying to do the same with our business model. And so this is something that's very different in what we're doing today, if you look at our partnership business, we've crossed the $500 million mark in inflows coming to the partnership into the company. And so that's really making a dramatic difference in how we're able to build and invest in the platform as well as a great contributor to the NPV, but then also on our internal pipeline. We've got five clinical programs, two and two preclinical that we own the rights to. And that's not in a single indication. It's not in a single technology. It's not a single scientific principle. And so what we try and do is say, some of that will be first in disease or first-in-class. Some of it will be best-in-class. We really want to take a risk diversified approach to how we're advancing that. This is coming back to our original mandate as a company. Was actually to take some of the binary risk out of biotech and make it into more of a business model. And so that's really starting to come together in a new way.

Okay. Great. So I know you mentioned Najat. So we've spoken with her a number of times. It's great to kind of see the company, I would say, 2.0 under her leadership. So kind of tied to the evolution of the platform, I guess, what are the most important kind of things to flag from a strategy leadership standpoint in the Najat era now? And maybe kind of tied into that, how we should think about even expenses, right, like cash burn kind of tied into all of this. As we look at your growing and expanding pipeline, how is Najat kind of approaching what Recursion is able to do with that pipeline through the lens of kind of burn moving forward?

Well, I'll start answering in reverse there. So everything that I just talked about, we did last year for about $400 million in gross spend, so not including any of the inflows from the partnership. The comparison in pro forma 2024, that was $606 million. So we took $200 million out of the expense base, while actually really expanding our capabilities and building out our clinical pipeline. And I think those two, the previous answer in this one is a great analogy to what Najat's focus on the businesses. So how do we get down to those things that matter most and be disciplined about making decisions around them? Not to invest in areas that have a low potential return, not to invest in things that don't have a clear clinical or commercial pathway. Like we have so many things that we could do. How do we focus on the things that we will do best and that we can have confidence? This is a bet that we should be making. And so that's been something that she's really come in. Obviously, she's got fantastic operational and scientific experience. And so really bringing the rigor into understanding how we're making our scientific decisions, what we're investing in on the technology side. She's both a coder and a chemist by background, and so that's been really a fact-based, data-driven, disciplined management style.

And you mentioned some of the partnerships now, too. And I think this is -- this is something we are kind of constantly trying to unpack on our side, both as analysts trying to actually build out some of these models and understand when and where the value is coming from. But also on the investor side, too, trying to get at really what is differentiated about like what Recursion is able to do versus some of the other AI players in the space when you're talking to some of these pharma partnerships. So maybe we can use the Roche PhenoMaps conversation as an example here. So I know you recently announced a kind of another milestone within that. So can you maybe just walk us through -- first of all, kind of just the economics of that particular partnerships and that particular partnership and how representative it is of Recursion's ability to kind of work with pharma. But then also, again, maybe extrapolate a little bit to what you're hearing in your conversations with pharma, whether that's Roche or others and how we should think about the capacity to partner with AI companies?

Yes, absolutely. And we get this question a lot because obviously, you see things like Lilly making very large investments into the AI space and as well as a number of other companies. I mean, Roche and Sanofi, who are our two largest partners have obviously made a lot of investments themselves. But they continue to invest behind our partnerships. So with Roche, in particular, to date, we've brought in $210 million from that partnership largely around the construction of Novel PhenoMaps towards neuroscience. So this is looking at cellular systems, transcriptomics, whatever other data sources we can create a map from to really understand what are some novel targets in neuroscience. This has been a corner of the industry that has had very, very few new targets over the last several decades. And so what we're finding is there are a lot of new potential targets that are coming up when you take data creation differently and then look at it differently. And so that's been the collaboration with Roche where we've had the $60 million in milestone payments coming in from those two maps. Right now, we're really looking at how do we convert some of those ideas into design programs. So actual work on creating the medicines from those ideas. And that has continued to move ahead very, very well from both sides. I think what's interesting, how we structure our partnerships is really, we try and get paid in advance for the direct costs associated with our development of the technology and or programs and then maybe have an early milestone like with our Sanofi collaboration. We have now hit five discovery milestones. And what we're really doing there is each one of those programs is an example of there was something along that target that had not been solved by industry before. And so this is a collaboration with Sanofi to say, can we do something better? Can we get to a target product profile that no one's ever reached before? The initial discovery milestone looks at it and says, we believe we got there. Now we have to verify that it's a drug that's going to move ahead. That's the next milestone each of them. What those collaborations, whether it's Roche or Sanofi lead to is once we've completed our aspect which hopefully either comes up with a new target and/or new molecule, then they would bring it in. So in Sanofi, those five programs, the next milestone is development candidate. That is effectively profit coming towards us because it ends our operational obligations and Sanofi carries forward and does the clinical development moving ahead. With Roche, what we're doing is converting those ideas into the design programs, which would then follow a similar trajectory. Now getting back to your original question of what does this mean for the industry? How is this differentiated work with us because we're not only creating great models. There are actually a lot of companies out there who can create great models. What you need to be able to do, you need to have a data set that actually powers it. So we've got tens of petabytes, over 50 petabytes with our own proprietary data that has been created by us in a way that is usable for machine learning and other AI techniques. That's so important because most of the public data out there is -- it's effectively dirty data. It's very hard to use. You need to do a lot of cleaning before you could use it for machine learning. And it's also going to point you towards those same things that have already been drilled into. One important thing a lot of people forget about, right now, about 3% of the genome has an approved drug for it. If you look at everything that's in development, you're going to be closer to about 10%. That's a tiny part of biology that we're playing around with. And almost all of the data that's in existence is focused on that part where it's already been something developed. And so if we want to start to address that other 90% that's still basically a blank sheet, we need different techniques to go into it. And so this is where our data creation, our models are being able to do this on an integrated basis makes such a difference because any one model system that you create is going to be messy. There's going to be a lot of noise. And so one of the things that we found in that integration that we talked about earlier, is being able to take multiple different modeling systems and compare them to each other, you can start to take some of that noise out. As the only other way to test it is experimental. And so what you actually want to do is create a great new novel modeling system, have other modeling systems that could actually help you say, is this noise or is this real signal. And then as late as you can get to that experimental validation.

Can I ask just maybe one last one here before we dive into the pipeline itself. How this answer shifts if instead of me asking about pharma, I'm asking about the big tech players. Obviously, NVIDIA made some waves in the past couple of weeks here. How should we kind of think about their impact on AI drug discovery players such as Recursion, but like where is their interest in all of this? And you're mentioning a lot of kind of recognition that a lot of the data sitting out there is dirty or maybe not as useful as maybe a lot of us on the outside would think just based on the sheer quantity that's available. So is a lot of this kind of identifying what's good and what's not, building out some of the model systems to be able to fill in some of those gaps? Like how does the answer shift when we're talking about NVIDIA or an AMD versus -- and their health care initiatives versus like a Sanofi or Lilly?

Yes. So actually, everything that they're doing, whether it's NVIDIA or Anthropic or different groups, in the end, it's very helpful to us because a lot of what they're designing for is actually either workflows or greater compute capability. And so the -- if you break it down, the underlying modeling systems, still need independent creation and validation. But that workflow that goes along the top, and this is all of the agents and the different aspects that are being built are basically as good as the foundations that you put them on top of, but you need that foundation. And so we look at some of the work that's going on there and fully embrace it and bring it in as quickly as we can because it just makes our job easier, but you still have to create those underlying systems. I think NVIDIA is a great one. I'm sure everyone saw some news. It was sad to see them go as an investor, but it was actually completely separate from our corporate partnerships. So we have an ongoing corporate partnership with NVIDIA that's doing great. And they shifted their portfolio strategy, and they're obviously focused on quite large investments right now. But we continue to be a really good partner. They -- we have one of the fastest -- I think it's technically still the fastest, though Lilly will take it, supercomputer and biopharma. That was all an NVIDIA collaboration, and we continue to build on top of that. That compute is essential to what we're able to do. I mean the fact that we have it in-house means not only can we do it faster and we have a lot of cost savings, but every incremental improvement that they can make on to that, all of a sudden, it amplifies our ability to use our modeling systems to search the data to really power how this all works together.

All right. So I want to now dive into the pipeline itself because there's a lot going on here. So maybe let's start with 4881 in FAP, right? So you recently put out 25-week follow-up data as is the MEK1/2 inhibitor. I guess, first, really quickly, how did that initial update last year kind of measure up to what you were hoping to see also in the context of the current standard of care and really now lay out for us what are the next steps for this program over the coming months?

Yes. I think the data itself was about as good as we had hoped. So we saw dramatic reductions in polyp count. This is a disease where because of a genetic abnormality in the patient that they were born with, they will continue to have cancerous polyps grow in hundreds or even thousands of numbers throughout their intestinal tract. And so what you're trying to do is control that polyp growth because as the physicians look at it, if the polyps look like they are becoming cancerous, that's when they're going to do resections. And what our goal is, is to minimize that impact to patients, not only try and control the polyp count and burden, but also limit the need for resections and other surgeries for these patients. And so we saw a more dramatic reduction in polyp count than it's ever been seen in the area. Also, we had the patients and we took them off drug for a 3-month period. And what we saw is those responses were stable over that period, which is a really massive statement because this is a drug that, in an ideal situation, you'd be giving to the patient chronically for their life. And so you want to see not only that mechanistic action, but also the durability with it. So what we are looking at now is we're engaging with the FDA to figure out what the right next steps are. I think the big question mark coming out of the data is just what does the regulatory path look like. And so because this is a potential first in disease drug, we're engaging with the FDA to find out what the right trial design will look like going forward. We also did a large natural history study getting back to some of the things on how we can make a difference. We did two, actually, one that was following patients to see. Do these polyps naturally go away? They don't. The other question, we were actually able to create a foundation model within a couple of days that looked at about 250,000 clinical records and said, how are these patients actually being treated? What are the doctors writing in the notes about these patients? What is the actual standard of care? Because nobody knew, right? This is not something where you've actually got a stable standard of care. And so that was incredibly important in us being able to look at what are these patients likely to see in the clinic? What's their experience going to be? What other drugs are they going to have? All of those different aspects.

Okay. So presumably now the update from these conversations with the FDA, first half would give you a sense of what -- when you could potentially start a pivotal study and what that study would actually look like. Okay. So then I guess kind of tied to this because I know you all have talked about the evolution of the cleantech platform within recursion platform itself. Is it fair to assume that a lot of those investments when you're looking at starting a pivotal study for FAP, does that mean that you could find those patients faster, enroll them faster and maybe get to an actual pivotal data that much sooner? I guess what's that kind of look like through the lens of recursion today?

Well, we definitely hope so. And we started to put out data. So cleantech is a relatively new platform. We basically created it over the last year for us. But we've already started to see results. So what we've seen is enrollment going about 30% to 50% faster than the baseline that we had previously, which is really exciting, and that's basically, and we gave an example of this in our earnings. We're able to dive in and understand every one of the clinical sites, the patient populations and basically design our sites and design our outreach to be able to reach those ones that have the most potential to get us better patient populations. This is something you would think you would do along with the CROs, but I can tell you now having been involved in it many times over, it doesn't usually happen. And the way that we're doing it is actually quite different. So that's one of the ways that we're driving it. The other is being able to say who are the actual patients who I think are going to respond to best out of it. And so that's really looking in and that can actually lead us down different indication pathways. We gave this example with our CDK7 molecule, where if you talk to any of the KOLs, if you look at any of the papers, everything drives you towards estrogen receptor positive, HER2-negative breast cancer, generally in the CDK4/6 refractory area. We said, okay, fine. The preclinical data supports that, that should work. But what else could we do? And so we looked at different patient populations. We looked at different CDK7 mutational profiles for real-world patients and how that affected them in different in different indications. And what we found is, actually, it made a pronounced difference in ovarian cancer. Now this is really interesting for a couple of reasons. One, there are a number of ovarian cancer patients who are also estrogen receptor positive. So maybe there's a potential link to why that could be more successful in breast cancer. But also, this is from our own anecdotal data, and I consider all Phase I trials anecdotal. From our own anecdotal data, we saw a partial durable response in a fourth-line metastatic ovarian cancer patient on monotherapy in our own trial. We didn't expect to see any responses because this is effectively a cytostatic mechanism rather than cytotoxic. So it's a cell cycle disruptor and transcription inhibitor. So that was really exciting. We were seeing it from multiple different angles. There is a logical mechanistic angle. There's a real world inductive evidence that it should have an impact, and we saw something coming across anecdotally in our clinical trial. And so we pivoted the clinical program to ovarian cancer. There are other indications that we could go after and expand into, but we felt like that was a good place to start.

And I want to ask a little bit more about the CDK7 drug. But just to follow up, so the 30% to 50% faster enrollment that you mentioned, is that in one specific drug, one specific program? Or is that kind of just across the board? And I guess I'm curious that 30% to 50% faster than what's the comparator here, like what you had been seeing in your own trials before you did this investment or what you've seen kind of broadly in each of these spaces?