Recursion Pharmaceuticals, Inc. (RXRX) Earnings Call Transcript & Summary

Good morning and [Technical Difficulty] Healthcare Conference [Technical Difficulty] love to have you in Miami. [Technical Difficulty]. I'm Mani Foroohar, senior analyst, genetic medicines here at Leerink, and I am obviously excited to have -- I also have some technical difficulty, Najat Khan and Ben Taylor from Recursion. Welcome, guys. How are you doing?

Great place for having this.

Let's start the conversation with a little bit of a strategy question, Najat, and this is a further conversation on the last earnings call. How do you think about the continued rationalization and narrowing and tightening of the portfolio? And should we expect future updates to look a little bit like what we saw in the earnings call with fairly rapid iteration on what the pipeline looks like on a Q-over-Q basis?

No, great question. Again, great to be here. Look, I think from a strategic perspective, we're really focusing on 3 areas. One is doubling down on those proof points. Second is surgically investing in the platform where you can really lead to that proof point. And then third is carrying that ambition with discipline. So just to answer that question, in terms of the pipeline, we have rapid go/no-go decisions across each of the programs. And there's a reason for that. We have a portfolio, which means that we want to make data-driven bets where we see the differentiation play out versus not. So I think that's number one. Number two, you also see progress in terms of our partner portfolio. We just shared for the first time our joint portfolio with Sanofi. It's about 5 programs where we are designing the molecules for challenging targets in I&I and in oncology as well. And then the third that you'll also expect to see in earnings, we track how is the portfolio doing, the velocity that we're starting to see in the portfolio and the platform specifically. For instance, we are synthesizing about 90% less compounds to those that actually go to advanced candidate and to the clinic in about half the time versus industry standards. So we think it's really important for us to be objective and data-driven on the program, partner program and platform. The last thing I'll say, and you heard us talk about the outcomes-based budget, this was a lot of work. We basically zeroed out the budget, and Ben can speak more to it. And we said, in order to meet the milestones and the catalysts we have, what is the fully loaded cost of each area. So it really helps to have a more objective investor-like mindset to say, okay, if I don't see the data here, I know exactly how much capital allocation I can extract to either extend the runway and/or apply to another program where you have more conviction. That's the approach that we're taking.

Well, and Najat hit on a really important point there because what we're trying to do is use our technology to enable us to do portfolio management in a more classic way. So because we're able to advance programs and get better data earlier, we're able to make better decisions earlier and move clinical programs forward. And so you look at our -- currently, we've got about 7 programs internally that are advancing, and we've got the partnership programs. You can expect us to be making not only quick decisions, but also a lot of transparency as we go through that program and advance it forward. And so it will look more like something that an investor would be used to, but all of these programs have something that our platform has contributed that we believe creates a better probability of success.

So let's talk a little bit about how that translates, how that flows through the financial statements from the pipeline. Obviously, you guys are engaged with the FDA actively with what is your current most advanced asset. Is it reasonable to assume that as individual assets move forward to pivotal studies, longer make as much sense, et cetera, that we're going to see tweaks around the margins on how you guys talk about OpEx on an annual -- on a quarterly basis? Or is it more reasonable to expect tinkering with how you talk about OpEx assumptions on an annual basis? Like how frequently should we expect you guys to be tweaking our expectations and sort of giving us feedback to make sure we model right?

Yes. I think what we'll do is we've provided annual guidance. And when we're making more significant updates to the strategic plan, we'll give you more significant updates to the annual guidance as well. But otherwise, you can expect that we're working within that guidance to try and execute on all of the different programs that we put forward. So for 2026, we said we expect our gross burn to be less than $390 million. That doesn't include any of the inflows from the partnerships. We do expect to get meaningful milestones coming in from those partners. We will treat that like I was talking about before, like a portfolio management area. So that we may take money out of some areas and put it into other areas. But if there's a major update, we'll give it to you.

I think you -- as you said, you're excluding partner inflows, can potentially be meaningful, but hard to predict with certainty. Let's talk a little bit about the opportunity and target landscape on the partnership BD side. Obviously, an opportunity to inflect those numbers near term and get some leverage off of other people's infrastructure into the clinical development. There's been a lot of discussion on whether or not the tech-enabled drug discovery field is "crowded". I'm not sure what that means. But how do you think about the dynamics in terms of the level of pricing power you can demand for your platform, how that evolves over time and the competitive dynamic with some other tech-enabled drug discovery companies, which are quite capital hungry and so clearly looking to partner compete for partnership volume with pharma as they attempt to fund themselves in what I think we can agree are fairly choppy macro markets?

Very choppy. Look, big picture, I'll start on that, and Ben, please feel free to chime in. I think the most important thing is not partnership announcements, but partnership value realization. And when I think about the landscape from that perspective, it's actually not that competitive. We don't really have a lot of companies that have actually shown that they can deliver and their pharma partners are actually paying them money talks, right? So at Recursion, we just crossed over $0.5 billion in upfront and milestones. So just to give you an example of that, one is with Roche, which is much more focused on -- I like to call our platform like a trifecta, vertically integrated trifecta, biological AI for novel targets, chemical AI, chemistry AI for small molecule design. We're not in biologics yet and then also clinical development AI. So the Roche one was for novel targets in the biological space, and we just received $60 million in milestones back-to-back for 2 novel data sets that actually generate novel targets which we're working on. That's one. And one thing that gets pretty misunderstood, I think that makes us different is not just the integrated vertical AI tech stack, but also the fact that we do all of the wet lab and dry lab. So to make these maps, we made 1 trillion iPSC-derived neuronal cells and the foundation models and the knockouts and now we're working with Roche, Genentech on the functional validation to say which insights are actually causal targets that we will collectively start programs on, very different. The next, I'll say, as you mentioned, Sanofi. We just received our fifth milestone from them where we have 5 different targets we're working on around lead series. So this is Sanofi's TPP and us delivering on that, and we have more milestones coming up with the development candidate milestone, which is the trigger to actually onboard it into their pipeline. So that's sort of what I tend to track, which is there's a lot of activity, but where is their impact. And having been on the pharma side for a long time prior to Recursion, that really matters to me a whole lot. And I think this debate [Technical Difficulty] to your point, we learn from some of the best partners that we have. And these are in the areas of neuroscience, oncology and I&I, pretty big areas. And last but not the least, it is also another dual track to validate our platform. We test, learn and then we scale.

Yes. Just a couple of points to add on to that. So if you think about how we structure our partnerships, we get paid upfront or in early milestones to cover all of our direct costs. And so that building aspect that Najat was talking about is both a platform and an NPV value that we're building off of basically without having to use our own capital off the balance sheet to make it. Now this is really important because about 2/3 of our spend is actually applied to our pipeline and partnership programs. That's including applied development on the tech side of the platform and our experimental work. So we really are gaining a lot from the scale that comes with that. But the financials are terrific, too. I mean the Sanofi programs that Najat was talking about, per program, we can get $343 million in potential milestones, $193 million of that is pre-commercial, so not a big bio drugs deal. And then if you think about the royalties, average royalties will be in the low double digits. So really nice strong financial relationship there. We've advanced those 5 programs through the first discovery milestone. The second milestone that's coming up is actually a development candidate. Now that's significant for a couple of reasons. One, it's a larger milestone. Two, it ends our operational obligations. So that's all profit that drops down. And every milestone that comes in after that has no offsetting expenses to it. So it's all going to be profit that drops down to us. We have room for up to 15 programs on the Sanofi partnership. The Roche partnership, I almost hate to say it, technically, it can go up to 40. I doubt we'll get to 40, but we've got a lot of room in both of those to dive in.

Let's talk a little bit about the underlying infrastructure that you guys are building these partnerships as well as wholly owned assets on. Obviously, the company has done a little bit of rationalization around number of sites, et cetera. Is there still room to run there? Or do you guys feel like you've established like a baseline level of sort of maintenance OpEx, platform CapEx, et cetera? Or should we still expect to see narrowing the geographic footprint, et cetera?

Yes. I mean I'll start, but I know this is close to Ben's heart. Just as context, we reduced our pro forma expenses by 35% to less than $390 million, and we shared that at a conference earlier this year. Number one, I think OpEx, yes, from a G&A and so forth perspective, we've brought that down significantly. We want to make sure every dollar is actually going to value creation. I mean this is operational excellence 101. I'm not seeing anything that exciting. We are going to continue watching that, number one. So expect that sort of discipline to continue, that has to be the case. The second thing that we're also doing is, look, the platform is never static. In this era of AI where there's constant innovation happening, the way Recursion has stayed ahead is by actually investing in the frontier areas, but you also have to balance that with where does it really matter. You don't want 1,000 flowers blooming. You want to make the ones that are the bottleneck in R&D. So we've taken a strategic look at that, and we're going to be very surgical in terms of where we invest in our platform. At the same time, we're also starting to see some of the velocity coming in and the efficiency from what the investments we've already made. Like some of the stats around, look, back to the trifecta. In the biological part of the platform, once you've generated that data, which in biology, one of the biggest issues is the data sets don't exist. This is proprietary. 40 petabytes of proprietary data, that's a lot of data. It becomes a search issue, right? You're just starting to search. You're not doing CapEx investment. You're actually just reusing that to understand, better understand relationships and validate them. Second thing, on the chemistry AI platform, it's -- I want to underscore that again, making only 330 or so compounds to get to development candidate, advanced candidate like what goes into the clinic in 17 months versus 2,500 plus over 42 months, which is industry standards, and I'm being generous, I don't think it's one yet, but these are green shoots and it makes us -- people ask me, how do you do that? You simulate more, you make less. That's how you also get efficiencies. So Mani, it goes both sides. We're going to invest, but we also expect to see efficiencies, what we've already built in and not just efficiencies, efficiencies that can lead to effectiveness in the clinic.

Yes. And I mean, if you think about it, we're a technology company. We should be getting more and more efficient every single time in every single new project we take on. And so as we look forward, we think about how can I make more tomorrow with less cost. And so I think to some of the points, like our data has become more and more valuable because we're able to mine and build and actually look at orthogonal data sets and orthogonal testing systems to be able to do more in a simulated environment rather than running to experiment. I think another important part is our CapEx spend. I mean if you look at legacy Recursion or Exscientia, both of them were in the tens of millions of dollars every single year on CapEx. Last year, as a combined company, we had $6.5 million. We're only going to have a few million this year. And that's because we've made the investments. We know what is valuable, and we're driving that forward to push programs ahead in the pipeline.

And if I can just build on that, you also want to make sure your investment, especially CapEx investment is future-facing. So we invested a lot in this wet dry lab loop. Not everyone is talking about it, but that's an investment we made years ago. The more important question is how do you use it effectively to make the right data sets that actually are useful to generate these novel targets. We're doing that internally, but then we also learned how to do that with the likes of Roche, Genentech. So I think it's -- like sometimes I get asked the question, what's the differentiation of Recursion. And you can talk about many things. But ultimately, it's not just the data. Data is a huge moat. Like everybody, most models are based on public data. Having 40 petabytes of private proprietary data is important, but it's also fit-for-purpose high quality. Models, yes, that's important. People, very important. Finding people who understand both tech and science, harder than it looks. But it's actually the integration of that vertical tech stack, right? The fact that you can go from biology to chemistry to clinical and back and forth and learn, that's where the effectiveness comes from. That's how you become and produce a more repeatable engine and not just a one-off.

Let's talk about the talent piece of that, now you brought it up. I know obviously, the debate about the struggle for talent in AI land is eternal. Sadly, no one's throwing $100 million at me. So for those who are listening, that would be okay. I think that's cooled down a little bit. But as you mentioned that overlap of technical and analytical skill and understanding of science, especially with understanding of drug discovery, which is its own unique art and science, how should we think about the pool of talent and managing and investing in talent as an asset and where we are in terms of the competitiveness of recruiting for that piece of the technology stack, labor capital, however you want to think about it?