Conference Transcript - RXRX 2026-05-12

Alec Stranahan, Analyst — Bank of America

Hi, everyone. Thanks for joining the session with Recursion, and thanks for attending the 2026 Bank of America Healthcare Conference. My name is Alex Stranahan. I cover Recursion and Smith Biotech at Bank of America, and I'm pleased to be joined today by Ben Taylor, Chief Financial Officer of Recursion. Thanks for being here, Ben.

Ben Taylor, CFO

Happy to be here. Thanks for the invite. It's always good to come out every year.

Alec Stranahan, Analyst — Bank of America

Yeah. Yeah, great. Well, maybe just to start, there's a lot going on at Recursion. This This is AI-enabled drug discovery for those who are uninitiated. And Recursion is really a trailblazer here. They've been doing it for many, many years. I guess, Ben, to start, you've got sort of three value drivers at the company. You've got your internal pipeline. You've got your pharma partnerships. And you've also got the Recursion OS platform itself. I guess, where do you see the biggest re-rating opportunity for the company? Is it from the pipeline validation? Is it from advancing and expanding your partnerships? Or is it really from the platform and sort of what you're driving from that?

Ben Taylor, CFO

Well, at the heart of it, we're a therapeutics company. And so we really focus on making sure that we're advancing the pipeline and getting value drivers from that. I mean, if you look at one of the fundamental tenets of the business is to create a more risk diversified biotech model. And so that has, over the last decade plus, involved a lot of platform and a lot of the partnerships as well. But in the end, it's all for the goal of actually developing therapeutics that are going to reach patients. And so right now what we have is five different clinical programs that all have clinical data in the next 12 to 18 months. that's really impactful for showing proof of concept and or we've got 4881 that's starting its registrational trial. And so those are really the core value drivers, not only from an investor perspective, but also just for the core mission of advancing medicines towards the patients. The other components, it's funny because for us, it's a continuous stream. We don't really view the platform separately from the pipeline and the partnerships. We're actually just doing the same work that we would do on our internal programs, but we're getting paid in advance for it and a nice MPB on it. So all of it really flows back to does this drug have differentiation and is it likely to be a good product for patients? Great.

Alec Stranahan, Analyst — Bank of America

And maybe for those less familiar with the company, maybe you can just talk about sort of the full stack capabilities that you have and how that's evolved over time, where you're sort of at today and how you sort of see yourself settling into the rapidly evolving AI landscape. Yeah.

Ben Taylor, CFO

If you take a step back and you think about, all right, how can you make a differentiated model in biotech? There's two components that you have to do. One is improve the probability of success. If you've got a 90 plus percent failure rate in the industry, you're always guessing. And so that's the part that really focuses on building a better data-driven model to it. If you think about how we continue to advance along and bring up the other pipeline programs, what we've been able to do is show that we can do that across a number of different facets on where that probability of success can come down. So if you think about a program as a whole, you need to think about the failure of a clinical trial as being based on statistics. It could be chemistry. It could be biology. It could be patient selection. It could be trial design. And so all of our systems are actually saying, can I create a better predictive model to be able to solve that reason of failure in the future? And so we have an advantage because we've been doing this for so long. we've been able to create lots of different predictive models to be able to say, I want to understand that patient better. I want to be able to explore the biology better. I want to go through a completely novel chemical space. And so there's not a single point of technology that really defines us. We just have the largest integrated tool set to be able to solve those problems that cause failure for clinical trials. So I think that is a massive differentiator for us, the fact that it's been created within a unified system so that it works together, where most of the companies in the space right now are really developing point solutions for a single area. The other aspect is the data. I mean, because we've been doing applied work for so long, we've created a massive data set, over 50 petabytes of internal proprietary data that allows us to build better models. So if you're just using the public data, or even if you're just using data that has been generated outside of the ML context, the fidelity of it's pretty low. And so it doesn't actually drive a lot of predictability. What you really need to do is create something that supports a better data analysis. We just had a paper published that showed a much smaller data set of highly annotated data actually creates far more predictive models than a massive data set of poorly annotated data.

Alec Stranahan, Analyst — Bank of America

Interesting. And I want to talk about something that you got asked at our breakfast this morning, and I thought your answer was pretty interesting. Just thinking in the near term, the industry is evolving in terms of where innovation is being rewarded, right? Typically that was through pharma partnerships like what you have. A lot of those pharmaceutical companies are going to China now as well. I guess on the AI drug development landscape with like AlphaFold kind of established now, where's the next sort of white space for development in the next couple of years?

Ben Taylor, CFO

Yeah, well, it's just, it's absolutely massive. So if you think about all of the pharma industry, all of the biotech industry for as long as they have both been running, and all of the approved medicines and all of the drugs that are currently in clinical trials, you only cover a little over 10% of the genome. And so that means, you know, almost 90% of biology is sort of in the dark to us from a therapeutic perspective. And the chemical space is actually in a similar state, if not even worse. And so, first of all, there's a lot of white space that we need to explore. And that's part of why we need new techniques to be able to evaluate it. But I really think where we're going to continue to see evolution is one over the next couple of years. well, where we are right now, people understand that patient selection is important, right? If you get the right patients in the trial, you can increase probability of success. And obviously, we've got a lot on that in Clintec. What I think people are just starting to understand is the chemistry space, which is really potency and even selectivity are only a small piece of the puzzle because realistically, how is it absorbed? How is it metabolized? You know, what are the other interactions that you're dealing with? And so we, in Legacy Xentia, had predicted multiple clinical trials that failed before there was any data on the clinical trials, just looking at the chemistry. And so I think people are just starting to understand, okay, a potent compound that works in mice, which is, by the way, a preclinical model that's basically designed just to focus on potency does not actually take into picture the entire patient environment and all of the biology. And so understanding that side from chemistry, I think, is just starting to come up. And then the biology side of it, people still think of very linearly. You know, ABC1 connects to XYZ2, and that's how biology works, which of course is not true. It's an incredibly complex multivariate problem. And so I think we are just starting to get the tools to be able to explore that better and be able to get into areas that are far off. What I was talking about earlier was really exciting. Virtual cell is obviously a big word often used, but the publication that we just had in Nature Biotech, basically what it showed is for a number of different cell lines, we were able to experimentally predict what would happen in that cell without having it be at all part of the training data. And so we're actually getting to the point where you can use multimodal biology. So cellular phenomics, which is what recursion was originally founded on, combined with transcriptomics, combined with other different aspects that you can bring in.

Alec Stranahan, Analyst — Bank of America

And all of a sudden that signal that's so cloudy in any single medium becomes much more clear and you can make predictions on actual underlying biology even without having knowledge it's still a long way to go there but we're just tip of the iceberg on it yeah and I mean the way I think about alpha fold is you had a good data set and there there's not that many I mean there's there's a lot of different ways that proteins can fold and interact, but it's not as complicated as like neutrons and electrons in like small molecules, right? So like the multi-modality of that made it an easier first step, actually, which was a little bit counterintuitive. Is the right way to be thinking about it that you're just going up in orders of complexity, going from proteins to small molecules and then to a virtual cell?

Ben Taylor, CFO

Well, it's really interesting. I think there is going be fits and starts across all the categories, but you're absolutely right in a way. The alpha fold, which by the way, completely amazing and fantastic discovery, but it was based on 30 years of annotated protein data that existed in the public space so that there was a much larger base to be able to build from. And then being able to put that together, you're basically looking at physics principles and how that combines together and it becomes a computational statistical problem. With chemistry, you're talking about, so just to put it into context, if you take all of the antibodies, for example, that are known and out there, you're talking about 10 to the 15th about. If you take all the potential medicinal chemistries, you're talking about something around 10 to the 60th. So effectively, you know, from what we're thinking about. The biology, obviously, you're interacting between all of those different pieces. But I think what's really important is alpha-fold itself didn't solve all proteins. It may be far better at estimating and predicting what then you can prove out experimentally later on in proteins that are not well known, but the prediction capability is far higher in areas where there's a lot known about proteins. And the same is true in chemistry, like generative systems work better around areas where there's been a lot of data created. And so what you'll see is there is going to be aspects of biology, let's call it the most complex, because you're now combining all of those different tend to the whatever it's. And you're going to have amazing breakthroughs in that, But they're going to be around areas that you can build off of, just like, you know, we're continuing to advance in proteins and continuing to advance in chemistry.

Alec Stranahan, Analyst — Bank of America

Great. That's really interesting. But maybe we can move from the theoretical now to the practical. You got me going.

Ben Taylor, CFO

Yeah, I think it's super interesting.

Alec Stranahan, Analyst — Bank of America

But in terms of what moves stocks, including recursions, it tends to be the tangible aspects of what's coming out of the platform. And for you guys, that would be your clinical and pre-clinical programs. So maybe we can just go down the list because you've got a bunch of interesting programs in your pipeline. Maybe starting with 4881 in FAP. For those less familiar, maybe just talk a little bit about sort of what this disease is.

Ben Taylor, CFO

There's really no approved therapies here, although there's maybe some context that you can put 4881 in. into that makes sense from a biologic perspective and sort of what's the the clinical course for these patients and what are you trying to to solve for with treatment yeah really really tough disease state so this is usually identified hereditary though there probably are a lot of somatic mutations that cause this as well but 50,000 plus and that's primarily the hereditary population, who throughout their entire life, as long as they live, they're going to continue to get hundreds or even thousands of malignant polyps throughout their GI tract. And so this usually starts up in adolescents or teens. Patients will usually have a colectomy before they're 30, and then they'll continue to get further and further resections as time goes on. They're likely going in and having excisions of all the polyps, can be on a several-month basis. So really, really difficult disease state, very, very high comorbidities, and completely chronic. So what we've been able to show with the data is within three months of treatment, the polyp burden was reduced by about 50%, And then we were able to actually also take patients off of drug and maintain that polyp benefit, which is really, really important for a chronic disease drug. Those aspects basically gave us confidence that we would be able to move forward with the registrational pathway. And so we're currently in FDA discussions on starting the pivotal trial, and we'll give another update on that later on this year. But huge unmet need. There's no approved therapies for it. And, you know, we're really excited because that was a discovery that came out of our BioAI platform. It was a novel connection that we were able to look and say, okay, how can we potentially reverse this APC-driven mutational effect with a drug and then convert that drug into now a patient proof of concept.

Alec Stranahan, Analyst — Bank of America

And I guess, you know, as we're thinking about the translation of polyp rebutin to functional outcomes for patients, I know you've got a pretty substantial kind of natural history set that's really helped you understand sort of the patients with this disease. I guess, how should we kind of bridge that gap between the 50% polyp revert into functional endpoints like, I don't know, CRC prevention and others? And what's most important for the FDA, I guess?

Ben Taylor, CFO

Absolutely. Well, so every single one of those polyps is precancerous. And so basically, if left untreated, all of these patients will progress into colorectal cancer. And so that's obviously one of the endpoints, but there's a number of different endpoints that you can look at with the FDA. There are things like there's a Spiegelman score, which is sort of a composite of polyp burden and dysplasia that leads clinical assessment and treatment. there are excisions because when someone without FAP goes in for a colonoscopy and gets a polyp or two removed, you know, it's painful, it's uncomfortable. When you're going in every three or six or nine months and having 100 polyps removed, you're talking about massive excisional burden, bleeding, infections, quality of life changes. And so you can look at those pieces. You can look at resections and serious surgeries. These polyps actually can go up into the duodenum and you can't do like a colonoscopy to be able to remove those polyps. You actually need a different endoscopic procedure, which is, you know, a far more serious procedure. So there's a lot of different true clinical endpoints.

Alec Stranahan, Analyst — Bank of America

If you talk to the clinicians, they view polyp burden as being incredibly important because it basically determines how they stage patients.

Ben Taylor, CFO

The other thing that we were able to do, and this is sort of an example of how we have the infrastructure, because we have a supercomputer, because we have AI scientists, because we have access to 300 million patients with real-world data, literally in the course of a week, we We created an LLM that looked across those 300,000 patients, found 250,000 patient records associated with FAP, created something that we could query and say, okay, what is the standard of care in the setting? How many surgeries are these patients getting? The answer is 10, by the way, across their lifetime of serious surgeries, not counting all of the colonoscopies. and that gives us a lot of insight that never existed before that we can also take to the FDA and talk to them about the pathway.

Alec Stranahan, Analyst — Bank of America

Yeah I guess how central is the the real world data package to your FDA discussions and you know does this reduce the likelihood of a randomized controlled trial?

Ben Taylor, CFO

Yeah well I won't get in front of the FDA discussions certainly but what I'd say is it's definitely informative definitely appreciated I think even if it doesn't so the FDA loves data and loves transparency anyone who thinks differently hasn't worked with them they love it but even if it doesn't make a difference with our FDA pathway it makes a big difference with how we design that trial, which patients we go after. We can do things like take a patient population and change our inclusion exclusion criteria and say, okay, first of all, how does this change my patient population commercial opportunity across the US and Europe? And then we can say, where are the sites with patients that have this sort of demographic? And then we can build in there. That's why we've seen a 30% to 60% improvement in our enrollment rates across our clinical trials where we're using our ClinTech. So it makes a big difference.

Alec Stranahan, Analyst — Bank of America

Yeah, and that should pay dividends across the pipeline when you're designing new studies. I guess on RBM39, this is your molecule that can, I guess, take a side door to shutting down CDK12.

Ben Taylor, CFO

We're expecting clinical data in the first half, I believe. you know what what's sort of the the first in human data that that you need to show to give you conviction to advance and is how quickly could you make this go no go on yeah absolutely so we rbm39 just to take a step back was a program that we founded with our biologic platform it was basically cdk12 has been a very interesting oncology target for a long time it's transcriptionally related. And so there's obviously a lot of opportunity with high mutational burden cancers, but incredibly difficult to target because the pockets of CDK12 and 13 look the same. You get a ton of side effects with CDK13. What we were able to do is say, how can we identify the same biology in another target? And that was a novel connection. And then what sort of drug could then reverse that disease impact? And both of those were discovered phenotypically on our platform, and then we optimized the molecule. So what we have been enrolling in is looking at patient populations that generally are going to be higher in high, like MSI high populations or different areas that are going to be potentially more susceptible to this type of drug. And we wanted to do like an early look, almost like a futility analysis and saying, okay, this is a completely novel target that no one's discovered. This is a degrader, which has its own benefits and potential concerns. And we're doing, you know, early dose escalation and trying to be really efficient with all of our money. I'm sure we'll talk about that at some point soon too. And so what we wanted to do was take a look and this was the first look we did was last week on our earnings call. So basically we had, we showed that we had PKPD that was dose proportional. We had the profile that we wanted to see, which was basically, this is a protein target that you'd need to knock down probably 70% plus on a pretty constant rate throughout the day. And we were seeing that exactly how we had designed it to happen, which is great. And we're just getting close to what would be the predicted therapeutic doses. This is monotherapy, but, you know, there's potential to see signal coming out of monotherapy because if it was a cancer type that's exposed to synthetically lethal mechanisms or other things like that, we could potentially see a signal. So that's where we're going next. The next readout will be a more thorough data set second half of this year and could be really, really exciting. that's actually a a very large addressable patient population has a different sort of splicing and stability disorder right and i guess on that point we'll be looking to to see which tumors you select i guess is there a decision tree that you make either around which indications to pursue like unmet need these are the market opportunity and then how do you make the decision to to mono or combo. Yeah, yeah. As you will respect, we are incredibly data driven. So we do a lot of real world profiling and statistical analysis around that. We also do that for commercial analysis. And we can do both of those aspects in house. And then we're going to be looking scientifically at the data.

Alec Stranahan, Analyst — Bank of America

We've got theories based on what we see coming out of the bio platform and what we believe to be true and we're going to be looking in the data and say what holds up and what doesn't okay and maybe shifting over to 7735 which you announced i think earlier this year this is your mutant selective pi3k inhibitor obviously a pretty hot space right now with cell acuity and others um relay etc i guess um here i guess you have the potential to address some of the safety shortcomings with the hyperglycemia and stomatitis that's been seen. Is that really the leg you stand on from a competitive perspective? Or is there something else you expect to gain from the molecule? And I guess what is sort of the IND enabling data you need to show to push this into the clinic?

Ben Taylor, CFO

Yeah. Well, I mean, there are other things than the hyperglycemia, but that's a really big one because this is always sort of the hidden secret in oncology drugs is you see the results come up from a clinical trial you should always go back and look at the inclusion exclusion criteria so most patients right now and we did this on a real world analysis as well i mean if you've got diabetes or pre-diabetes which is actually in a lot of the cancer indications around half of the patients you're not going to get this drug or you're not going to get a PI3K that are currently out there, or you are likely to come off it very quickly. So the real world times of being on, for example, PicRae is only a couple of months. And that's because of side effects profile. Even if you don't have diabetes or pre-diabetes, you know, grade three hyperglycemia is a very serious issue. And so that is a really big important part um i think what is also interesting is by having um so we're seeing 100 150 x selectivity over wild type now what that means is an ability we believe to drive deeper into the dosing of it for the mutants and so i think there's two levels of the question one is that orphan population that is not eligible for these therapies right now because of some sort of glycemia issue. But the other aspect is, can we go deeper on dose to drive higher response rates in the population before you start to get to MTVs? And so we've seen a lot of drugs come up in the space.

Alec Stranahan, Analyst — Bank of America

Our original thesis on this continues to hold, as far as we can tell, we are about an order of magnitude more selective than the drugs that are currently advancing through development and so we like this drug okay so we'll stay tuned for the data from that program in the second half I guess I want to ask about another arm of your business which is partnerships that the two main ones that we hear talked about are Sanofi and Roche and maybe we can roll this into sort of the capital allocation burn question which is um how could milestones potentially

Ben Taylor, CFO

bolster your cash position over the next 12 to 18 months how are these partnerships going and how do you sort of see the allocation between your internal development you know servicing your partnerships and even good stewards of capital yeah and um i'm gonna back it out into the capital allocation question as the core part to answer there because i think As I said earlier, we're a therapeutics company, and we think the long-term value is going to come out of the drugs that we develop. And so if you think about how we invest in platform and what we do with our partnerships, it's got to be towards driving that bigger goal. and so first of all on sort of the expense management what we did post the merger was take a look at everything across the company and actually put a analytical framework to measure impact and basically said anything that we can't clearly see high impact from or high probability from, cut it. That literally took out 35% of the budget over the last year. And so we continue on with that framework now. Our expense guidance for 26, even with the five clinical, two preclinical, the big partnerships with Sanofi and Roche, it's still, so it's less than 390 million. We're focused on getting to that less number because we are, at our heart, a tech and efficiency company. We should always be getting more impact for less cost. We're going to continue doing that. We're also going to continue to be really disciplined in how we make decisions. So right now, most people don't realize this, about two-thirds of our cost, almost 70%, is going directly into pipeline programs and our partnerships. So there's not some big, you know, 30% of the budget going into platform development or something like that. That's not how we do it. It's applied platform and pipeline development. And so, you know, what we like to be able to do is say, okay, is the pipeline advancing? Is the partnership advancing? And if it's not, I'm going to cut all of the associated spend with that. And if it is, great, because it's going to be generating value on its own. And so from the partnership side, we've brought in over 500 million from those partnerships. You know, just in the last about two years, we've hit seven partner milestones, five with Sanofi, two with Roche that, you know, continue to bring in more money. And over time, that business right now, we run it basically to the break even to a mild profit because our partners prepay us for our expenses. as we get to opt-ins on Sanofi, as we get into advanced states on Roche, actually that becomes all profit that rolls in on a go-forward basis because we have no more operational obligations.

Alec Stranahan, Analyst — Bank of America

Okay, great. Well, there's obviously more we could talk about. Always a lot going on at Recursion, but I think for time we'll leave it there. So thank you, Ben, for the great discussion and thanks everyone for your interest in Recursion.