Recursion Pharmaceuticals, Inc. ($RXRX)

Good afternoon, everyone, and thank you for joining us on the first day of Needham & Company Healthcare Conference. My name is Gil Blum, and I'm a senior biotech analyst here at Needham & Company. It is my pleasure to have with me today Recursion's Chief Financial Officer, Ben Taylor. As a reminder, any viewers who are watching through our conference portal are able to ask questions via the Ask a question box below the video feed window. So Ben, maybe just starting with a bit of an introduction, setting the stage and just discussing some of the company's priorities in 2026.

Sure. Thanks, Gil, and really appreciate you having us at the conference. So '26 has been -- it's really, if you think about it, a year that last year was transition because we had the merger that happened towards the end of '24. And so we were doing a lot of reshaping the strategy, reshaping the financial structure, putting together the right pipeline. Now we've started to enter into that phase in '26, where we're looking at data readouts and partnership milestones on a more and more regular basis. So we already had our first proof-of-concept data readout with our FAP molecule and program moving forward, and now we're in FDA discussions on that. We've got 4 more clinical programs that are moving in besides that -- behind that. But then also our partnership business, where we've already brought in over $500 million through our partnerships and moved 5 candidates forward in early discovery with Sanofi, brought in over $60 million from MAP milestones with Roche. We want to see a more regular cadence of all of that coming through. On that backdrop of data, we also have a lot going on with the platform. So we've built out Recursion historically was always focused on phenotypic discovery. On that biology discovery, we've built in transcriptomics. We've added in using real-world patient data and really are focused on that multimodal integration to be able to do better biology discovery. Obviously, bringing in Exscientia changed the profile of being able to do chemical design. And now we can discover the targets as well as build the compounds, but then also quietly and you were one of the first to notice it, Gil, quietly in the background, we've also built up a pretty robust cleantech business that's allowing us to design better programs, understand the patients better. We've seen real data where we're improving enrollment by 30% to 60%. We're being able to identify sites in a better way and really understand the patient populations in a better way.

So the way that we view the business model of Recursion included 3 major components. So the internal pipeline, the pharma partnership and maybe the Recursion OS itself. Can you rank these components as it relates to your value creation? Who's your favorite child?

Well, it's funny. Our original mandate as a company was figure out how you can make a more risk diversified way of advancing biopharma. And a lot of our original investors, not only did they embrace technology, but they didn't like the classical model of biotech investing where you have this binary risk. And so that's why we have the diversified business model that we have and different ways of bringing it in. And even inside of that, if you look at our internal pipeline, some are more focused on the biology. Some are more focused on the chemistry or both. We didn't want anything to really be limiting us. Now if you look forward, I think the partnership business obviously can be more of a steady rolling business than the biotech, which is very up and down based on data, the internal pipeline part of it. But they also have different value propositions. I mean if you look at the biotech industry as a whole, good proof-of-concept clinical data can be absolutely transformative. And it's much harder to do that with parts of the partnership business. And so we want both. We want to be able to build our platform and bring in cash inflows through the partnerships. That is scale and capabilities that would exist regardless of if we had a partnership business or not. So why not leverage our ability to do things faster and better and cheaper to build out a partnership business rather than just focus it on our internal pipeline.

So another key differentiator, at least in our view for Recursion has been the ability to industrialize data generation at the discovery stage. So are you seeing some of your competitors, pharma, anyone doing this kind of investment these days? Things have changed in the last year, I would say.

Yes, yes, absolutely. Well, I mean, this is funny. This is why I think if you look at the NVIDIAs and the Googles of the world, the reason they're such good operating partners for us is if the entire industry did business in the way that we do, it would absolutely be one of their largest segments. And so what we expect is that the rest of the industry will start to adopt it more and more because we are just showing that it can be done not only faster and cheaper, but really to get to results that hadn't been achieved before. And so Lilly is the most notable party stepping into that and investing some of their good GLP money into building out the business. But I think you'll see a lot more. The reason you'll see a lot more is if you take all of the drugs that are currently in development or have been approved historically, you only cover about 10% of the genome. And that's not even talking about all of the diversity that each one of the genes can put out. So really, 90% of the -- of biology has not been explored from a pharmaceutical sense. And that's because we have the same techniques. We have the same data sources. And so you're going to end up drilling down the same holes and getting to the same answers. If you want to start bridging out and actually exploring the other areas, you need new ways of doing it. You need new ways of creating and analyzing data. And so that's what we've been doing for over a decade. I think you're starting to see other people really understand that and start to adopt that. The good news is even if everyone switched over to our way of doing business today, you still have 90% of biology that's unexplored and so much to do around chemistry as well. So there's plenty of room for all of us to play in.

So related topic. So tech space has this attitude that everything is possible to do anything. But this assertion is reliant on large linguistic data sets that are offered by the Internet and human activity, which we both know is not that long term. So we don't think there's a true parallel data set in biology. Do you guys agree with that kind of line of thinking?

Yes. And it's really interesting because almost building off of that last point, the data has got to be at the core. I mean the reason you talk to ChatGPT and that can give you deep social advice is because there is a wealth of data on how humans interact socially. You just do not have that on the biological or chemical level. I mean chemistry is -- there's 10 to the 60th potential medicinal chemistries. We've barely even hit a drop in that and biology, I just made the point on. So you really need to be creating differentiated data to build models on. And the models themselves like how you do the models. There is still a lot of capability and differentiation in the teams of people that can do it. But over time, they will commoditize more. And then you're going to be focused more and more on how do I get differentiated data to get to a different answer. And how do I integrate those things. One of the things that makes such a difference to us is we are not a point solution. We see this time after time where, okay, let's take what Recursion was originally known for, the phenotypic discovery platform. It's a great platform. It's a totally different way of looking at biology and using -- allowing the cell's biological process to be as complex as it wants to be in doing an image analysis of it. However, even that data set is going to have a lot of fuzzy data in it as every data set does. And so what you need to be able to do is say, okay, I think I'm seeing a signal here. Do I see the same signal here in transcriptomics? Do I see the same signal here in real-world data? Can I create some experimental system that will support it as well? And it's at the integration of those different pieces that all of a sudden, you get much, much higher predictive probability. And so you have to have the data, you have to have different orthogonal data and then you have to be able to put it together. And I think that's really what makes the difference. We've also seen a very stark difference in ability to learn and develop quickly if we're actually doing it on applied work. And so almost all of our spend, it's at least 2/3 of our spend is dedicated to applied programs in our pipeline or our partners' pipeline. And so then we have a real endpoint that we're designing and building and understanding. And so we can iterate on our models, we can iterate on our data and get better at it. I think it's really hard to do that in a vacuum because you always think your model is more predictive than it actually is when you put it to the test.

And maybe a last one on this topic. So we get asked a lot if drug development is a killer app for artificial intelligence, aren't more investors interested? Or what do you think the disconnect is?

Well, this has been fascinating for me. I mean, watching it evolve for a number of years now. What we ended up having to do was use that balance business model to be both the vendor and the customer in validating what we're doing because there was so little understanding of how it could impact different parts of drug discovery and how that would translate into development and such a massive established understanding of this is the right way to do business that we just had to be able to do it on our own. So part of the reason that we started our own internal pipeline was so that we could manage that process and validate, hey, we are getting differentiated chemistry or hey, we do have a novel biological insight and drive that forward and be able to talk about it. And so this has been a really interesting process. I think someone who is more comfortable in the tech environment is more used to investing in sort of the potential of the market opportunity and trying to think about total addressable market and where do you go with it. I think in the biotech sphere, it's always been data based, right? Like what is the data say and what can I draw from that. And so to be able to transition from what was originally a much more innovation and tech-based shareholder register to really integrating more of a balance profile along with biotech investors has required us to create data and be able to talk about data. And that's what I mentioned in the first comment. We're really just starting to turn over the cards that biotech investors really care about. And then I think, look, if those data points go well, it will be hard not to say that AI played a key role in it because it's everything that we do and the points of differentiation that it would be successful on, those are things that we try to solve with AI.

Do you think there's potential for like an aha moment like a drug that would go into market or some larger move as it relates to the space because one feedback that we do get is there's just no proof-of-concept that I would argue a little bit otherwise. I mean, there are marketed drug and placebo [indiscernible] comes to mind that are generated this way. But what do you think is that aha moment if such thing exists?

Yes. It's interesting. I think that initially, it will probably -- from a biotech investor viewpoint, I think until there's a couple that have really shown differentiation based on something that the rest of the industry couldn't do that people still just look at it as individual products. And to some extent, that's okay. Look, if we have successful products that are going through, the value attributed to that will help us continue driving the value of the business overall. But I think at some point, there is a aha -- I just -- I don't know if it's a wave craft. I think it's more of a distribution that happens over time rather than a single event like we saw with OpenAI. I'd love to see a ChatGPT moment. I just am more skeptical of that happening.

Moving from philosophy to practice. Collaborations, what can you tell us about expected milestone payments from your pharma partners if you have any visibility this year? And can we anticipate larger payments for foundational work kind of like what you've shown with your neural MAP...

Yes, really, really good question. So our partnerships, our primary partners are Sanofi and Roche. They both continue to go really well. With the MAP milestones coming in from Roche, we're at over $210 million coming in from Roche. I think our real focus there is let's translate the MAPs, which had never been done before and are really focused on entirely new targets in neuroscience, let's translate that into programs. And so that's a core focus for ourselves and Roche this year. And hopefully, we'll have some good events on that. On Sanofi, we've -- that partnership is quite different because that came into it with, hey, here's a target we want to do. We, Sanofi as well as the rest of the industry have never been able to solve problem X. And that could be targeting the pocket or it could be, you know what, this is something that's a big multibillion-dollar industry, but there are known issues with the drugs in that industry. Can you solve it. And so that's a different type of problem. But the fact that we've already advanced 5 of those candidates through early discovery now -- so what that early discovery milestone means is we think we've solved the problem. Initial testing, the preclinical and nonclinical testing has shown that, that problem appears to be solved or the problems. But now we have to make sure it's a good drug that will go into human testing. And so that's what the difference between the early discovery and the development candidate milestone is. Those development candidate milestones, which are the next ones for all 5 are larger milestones. The other really nice thing that I love is they enter operational obligations. And so that's all just profit that drops down to the bottom line. And then every milestone that we get after that is profit as well. And so we -- for each Sanofi program, each one can have $343 million of milestones, $193 million of that is pre-commercial. So this isn't massively back-end loaded And then we've got just beautiful royalties on it that would average in low double digits. Now we haven't given explicit guidance for what we expect as far as upcoming milestones and timing. But I would say both of those partnerships continue to go well, and we're just going to keep on trying to both expand them as far as expanding more programs that are running through them, but also just executing on that pipeline and hitting milestones.

Very closely related question. Should we expect any clinical opt-ins in the near term? Are we reaching that level of fruition? This is something we specifically pay close attention to.

Yes. So from our internal pipeline, you mean?

No.

From the partners.

From internal...

Yes, yes, yes. We can answer both questions. We can answer both. So on the partner programs, so when they do development candidate, that is essentially saying it is -- they'll still need to do some IND prep work on it, but you wouldn't hit that milestone unless you're intending to take it into the clinic. And so that will have a milestone not only at the development candidate, but then also in the Phase I. And so we can start to see those come in. But it would be really surprising for us to hit a development candidate milestone and then not have them take it into the clinic.

That is actually really helpful. So I mean you touched on this, but maybe just to form it a little better. How should we view cash flows from partners over time?

Yes. So what we would like to do, there's always the question of expansion versus profitability. So the way that our partnerships are structured, we want to get paid upfront for our direct costs. So we don't want to be investing cash off of our balance sheet for our partner programs. And so we try and always run our partnerships at a neutral to mild profit until they start to hit the development candidate and beyond for Sanofi because then they can become very profitable. And then it's a question of, all right, how many more programs do you want to continue expanding? Because then obviously, you're not really reinvesting your profit, but you're delaying the margin associated with it is what effectively happens, but you're also expanding your pool of potential milestones and downstream payments. So that's what we're always thinking about. We don't have a specific hurdle for either Sanofi or Roche. What we really focus more on, is this a good program? Like if we're successful technically in what we do, which we're usually pretty good at, is this going to be something that Sanofi and Roche are going to want to put their entire franchise behind and build up because that's when we're not only going to hit those development milestones, but that's where the royalties can kick in downstream and get really, really profitable.

So we do hear reports on pharma and large tech collapse every other week now. How can a small biotech like yourselves compete in shifting attitudes in pharma? You kind of touched on this, but recent reports on in silico come to mind.

Sure. Well, first of all, I think there are -- there is room for everyone's different business models that they want to run in it. I think in silico is a little bit different in that they probably go after a broader swath of potential partners than we do. We've been really trying to focus on what's the maximum value that we can get out of each individual partner, which actually generally means a little bit more exclusivity around each individual partner. Like I have no doubt that Sanofi and Roche pay us more because we don't have 20 other partners that are going along with it. So in that sense, I just think of them as different business models, not one is better and one is worse, but just different. We also do a lot more in-house sort of data generation and sort of proprietary biology, different aspects like that, that make it a little bit different in how we structure things.

And maybe last one, just a thinking point as it relates to additional strategies, just given the power of the platform, how do you guys view business development opportunities? You have like a relatively strong engine for vetting things.

Yes. No, absolutely. Well, the -- and it's interesting because we now have biology chemistry and sort of the clinical and real-world data side of it. We have a lot of different ways that we can contribute value. And obviously, FAP was an in-licensed program. We had -- the biology platform gave us a novel insight that no one had ever figured out before. And so that was before Recursion had a chemistry arm. And we went out and in-licensed a really nice compound for it, and that's worked out really well. And so there certainly could be other opportunities like that where we just have a differentiated insight on it. And we'll keep looking around because there's a lot of different ways that we can bring value.

This is a great segue. So let's talk a little bit about the clinical pipeline, starting with FAP. We're waiting on a regulatory update. But what do you think is the best case scenario here?

Yes. So the best case scenario is wherever we end up. I feel like we've got a lot of different ways that we can optimize around it. So if you think about it, there's a couple of different levers. One is what are the clinical outcomes. And we may be able to move some of the precedent that has been there before because no one's ever done the natural history studies that we did. Nobody has ever showed the depth of response that we did. No one's ever showed it as fast. No one's ever shown that they can maintain that response over time. So those are 4 new pieces of data coming in for the FDA that we can talk to them about and potentially that changes what a good clinical outcome could look like. And that's one thing to talk about. But even if it doesn't, what we really want to do is understand which are the right patients to be treating, where is the highest unmet need. And we've been able to demonstrate that we can enroll these patients faster. It's very interesting actually because a lot of people don't think about it, but the FAP patients are going into their gastros like every -- it can be every couple of months, and they do this for decades. And so actually, we have a really good sense of where all the patients are and who their doctors are and how to get to them, right? And so being able to use those sort of levers to drive enrollment in clinical trial and trying to decide which of those patient categories we want to target in the clinical trial. I think those are all important questions. There are also some around where in the treatment stage do you want to engage. But it's interesting. That's not a normal situation here because, let's say, a lot of people ask us about pre-colectomy or post-colectomy. Even if they're post-colectomy, those patients go on for decades where they're still going into their gastro every couple of months because the polyps actually just continue to expand. They go upper and lower then they'll go into the pouch, they'll go into the duodenum. And so you have to just continuously be going into the doctor. So there's not a segment of the population that doesn't have need. 100% of their polyps are precancerous. So I think it's a matter of us trying to decide where we want to target in the clinical trial and how we want to do it.

I mean some of the past experiences really did revolve around polyp counts as a key measure. But like you said, there could be ways to look at it in outcomes and does it postpone [indiscernible] colon, et cetera. So clearly, there's several potential outcomes here. When do you guys think you're going to be able to tell us?

Yes. Well, so we're in process and going through everything. I'll never get in front of the FDA, and they can -- they've been good partners with us on everything. We haven't seen disruption come out of any of the recent events. But you also never know until it's done. So we'll just -- we'll let the process continue to go and provide investors with updates as we have them to give.

And as it relates to clinical updates, when should we expect additional information?

Yes. So the 4 other clinical programs that are advancing CDK7, that's got combination data coming first half of next year. Really exciting there. The -- if you think about the CDK4/6 class, I mean, that's almost a $15 billion drug class right now. CDK7 should have broader applicability, but hitting that therapeutic index is the tight window. So that's what I'd say is a really high-risk, high-reward outcome, but that would be really exciting potential combination data first half of next year. MALT1 is also first half of next year. That's one where we've seen in other compounds a biological effect. So a good monotherapy biological effect, also combination therapy biological effect. But all of the drugs that have been developed to date have a hyperbilirubinemia issue, which is a real problem because clinically, that would almost always be used in combination with drugs that cause liver tox. And so we think we've designed that out, and you'll actually know pretty early on, right? Like it won't take that many patients to say, are you having UGT1A1 issue or not. And so that data, we're excited about coming out. I skipped over RBM39. That's the nearest one. And I'm sure you will ask a question on it if I don't bring it up anyways. So first half of this year, we'll be giving an update on safety and PK. It's probably an earlier update than people are used to, but this sort of underscores what we want to do is treat this like a business model, not like a pet science project. And so kill them fast if the data doesn't support it. This is a target that's never been drugged in this way, and it's a degrader. So you want to see good safety, good PK. You want to know that you're getting the selectivity and that you can keep going. And so we're going to take a look and hopefully move it on. But if it's not good, we're going to kill it quick as goes for all of our programs. And then we would expect the PD data and anything else that we can present coming later on after that. And then LSD1, which is actually a really cool program as well that is just kicking off into the clinic. So that's probably late '27. But this is something where a lot of people have -- we know that LSD1 works but causes thrombocytopenia. And so everyone is basically taking their LSD1 out of cancer and put it into diseases where you get too high of platelet levels. If we can get that therapeutic index level right, then it could be a really nice drug as well.

And maybe another moment of philosophy just because I can't skip it. Is there any concern that once you start generating value from your clinical programs, for example, FAP, you're going to start being valued only on those programs. You'll become a rare disease company instead of a platform company.

What I'd love is just in balance. So I think that what's interesting is we actually appeal to a much broader investor base than a traditional biotech. And so historically, as I said earlier, we really appealed to a lot of the tech and innovation investors who saw what we can do and said, you know what, if you're actually able to scale that, then it could change the industry, right? Like that was a lot of the original background. At that point, it was too early for biotech investors because we didn't have enough data to be able to dig into. I think what we're seeing now is that data start to come through. My guess would be the first drug that comes through, we're probably going to be valued more on that first drug, certainly by biotech investors. But if we start to have multiple drugs that are showing success, then it's going to be hard to draw a different conclusion than the AI is making a difference. And so that could be -- if you want a inside a single company, watershed moment, that could certainly be one of those.

An item that's near and dear to your heart. So control of spend is a recurring theme for Recursion investors. What's your strategy on those?

Yes. Well, I mean, hopefully, people noticed we took 35% out of the expense base last year while integrating the companies while advancing all of these programs. This is -- this was a huge priority for us. And the way that we do it is actually just by making much better decisions. And so what we did last year is we actually rebuilt all of our back-end systems and put in place an outcomes-based budgeting and business model. And what that allows us to do is break down and individually say for each program, this is what we're spending on it for every dollar that we have, this is where it's going. And to do that with very high fidelity. And so what that means is we can look at it and what we did in our budget projections, for example, is we assumed that all of the clinical programs go forward. We probability weighted the partnerships and our own internal discovery efforts. But if we decide no, a program isn't going, we know every dollar and every operation that we need to shut down on the other side of that, right? And we can go in and be targeted and be fast. At the same time, we are also just going through every operation that we have. There's not a part of G&A or tech or clinical or anything else that we do that isn't constantly testing itself, can you do this better, faster, cheaper, right? We obviously know a lot about AI. And so we've been an adopter of identifying. We've been an adopter of automation. And we actually utilize the knowledge and resources that we have across the company to help bring down that spend wherever we can. But it's literally just an ongoing, that's how we do business attitude across the company.

And as it relates to your cash runway and potential financing, how do you view this? How are you guys looking at things?

So the guidance that we gave for 2026 is less than $390 million in cash expenses. And that is not offset by any inflows that we have from our partnership business or otherwise. So that's just raw cash expense. I am very focused on bringing that number down, and I know Najat is as well to below $390 million and keeping to modify that. Obviously, part of that will depend on data and where things go. But if we look forward and we assume that all of our clinical programs are going ahead, we can see cash runway to early '28. And so I think we would like that to be longer, and we control part of that with our spend, and we'll continue to manage that, as I was talking about. The other part is we're going to continue to figure out how we can best finance the business going ahead. I mean, in the end, we're a biopharma, and that's part of the industry. So no specific guidance beyond that.

All right. We're pretty much on time. So maybe if you have like a last point, something that you feel investors are missing or anything that we didn't discuss that you want to highlight?

Yes. I think the only point that I'd bring up is a lot of people are out of date on the story is what we find. They view us as a phenotypic discovery company or they know maybe 1 or 2 of the older programs. And we've just changed so much. I mean it's not just the management team. It's also parts of the pipeline, it's parts of the platform. It's the strategy as well, and it's continued to evolve really. And we've learned from the things that we did well and the things that we didn't do well, and we've really come out of it as a different and better company on the other side. So I hope people are paying attention.

Right, Ben, thank you.

Terrific. Thanks, Gil. Bye-bye.