Recursion Pharmaceuticals, Inc. ($RXRX)

3 Great. Thanks, everyone, for joining. My name is Scott Schoenhaus. I am the health care tech equity analyst here at KeyBanc. We're a pleasure to have Ben Taylor, CFO of Recursion for a fireside chat. Ben, maybe it's helpful to sort of introduce yourself and the company for anyone that's new to your story. And you've gone through a lot of changes over the last 12 to 24 months, too. So it's worth highlighting your story to anyone that's new to it.

Yes, of course, happy to. So my own background, I was actually coming over from the Exscientia side of the merger. So I was the CFO and Chief Strategy Officer over at Exscientia and had been there for a little over 4 years when we brought the companies together 18 months ago. And before that, I had actually run the day-to-day operations at an oncology biotech. So got to see what it was like really digging in and setting up clinical trials, going out to the clinical trial sites, working with the FDA. And it was actually really interesting because it was what led me into the AI-based drug discovery. What I realized is I and all of my colleagues around me and a lot of -- so I've been in banking before that. A lot of my clients had really been guessing because the data that you have to make good decisions is so sparse. And so we'll take data that's coming out of a few animal models or a few unrelated studies and try and make a guess as to what's going to be a successful trial or drug or patient population. And so when I was leaving that oncology biotech, I thought I wanted to do some sort of role that was more data-driven. And that really led me into this space. The Exscientia side have been working on doing that in the chemistry space. So a lot of people focus on the biology component of biotech. But in fact, once you think you have an idea for a good target, a lot of trials actually failed because of the chemistry side of it, because either everybody knows about the potency or selectivity. So are you hitting your drug and your target and not hitting other ones. But there's so many other properties that go into it being absorbed and how is it metabolized and the toxicity profile of it. And that's something that AI really enabled us to do a much better job of doing a multiparameter optimization. So combine that then with the legacy recursion system, which had been focused on how do I come up with new ideas in biotech because -- right now, about 3% of the genome has an approved drug associated with it. If you include all the drugs that are in development stage, you get a little over 10%. So that means about 90% of biology is effectively unexplored. And even the 10-ish percent that we are exploring, I mean, there's multiple proteins that come off of all of those genes and a lot of those drugs aren't ideal. And so what we wanted to do was put those things together and say, how are we going to find new biological targets because you need to have new ways of creating and looking at data. And so that's where we've built our phenomics system. We do a lot of with transcriptomics. We do a lot of real patient data. And then how do you create a better drug for it. And then the third part that we actually built internally as a part of the combined company was really what we call our ClinTech business. And this is using real-world data to analyze both patient selection, so trying to identify which patients will respond better to our therapies, but also optimizing the clinical trials. And this is where we've been able to increase our enrollment rates by 30% to 60% by just taking a different approach to how do you identify the clinical trial site? How do you work on identifying the right patients for the trial. So we've been able to put those 3 pieces together, and I think that's been -- oftentimes, we're asked what's your differentiator? I think classically, it was always -- we have a lot of proprietary data, and we've got a lot of validation cases, both through our internal pipeline as well as our partnerships. But I think over the last 1.5 years to your point of bringing the companies together and building it out, we've actually created a really substantial differentiation by integrating all of those systems so that they work hand in hand. And we're not a single point solution.

That's really helpful for that context. Then I guess maybe talking through that, you've done some recent portfolio consolidations. Maybe talk about the strategy there? What inclines you to license your assets to other companies or shelving it altogether? And then how do you think about advancing certain other molecules like REC-617. So walk us through that strategy.

Sure, of course. So when we came together, we had a massive pipeline of about 10 clinical agents and far more than that on the preclinical level. And that was just -- it was too much for us as a single company to take forward. And we do have some large partnerships. We brought in over $500 million from our partners on that. But just focusing on our own internal pipeline, we said we need to be really focused and bring the highest impact programs forward. So what we did is ground up, look at the science, but also look at what's the clinical pipeline or clinical program for it. What's the commercial opportunity? How differentiated is that product going to be? And then do we have aspects of our company and our platform that are really going to make a difference and improve our probability of success. And so based on any of those things, it could be the data, it could be the commercial opportunity. We tried to eliminate a number of those programs. And now we've got 5 in the clinic that we have taken forward. We've got about 15 programs in discovery between our own and our partner programs. And the partner programs, we didn't do the same process too because with the partnerships, basically how that's structured, we get paid in advance for our direct costs associated for it and our initial milestones really cover any additional costs that would come into it until they get to development candidates or whatever the major milestone is like with Roche, we brought in $60 million for map delivery, right? So when we start to hit those milestones is when the profit starts to come through, but we don't have the same capital restraints that we do with our internal pipeline.

Maybe walk us through on the partnership side. I mean you recently achieved your fifth milestone from Sanofi in February. You have 15 best-in-class or first-in-class programs across oncology and immunology. How do you see partnerships further progressing from here?

Yes. We love our partnership business. So our main partners are Sanofi and Roche. We do also have smaller partnerships with Bayer and Merck KGaA. But as you brought up with Sanofi, we hit the initial milestones on 5 programs. What that really means is it was a program where the industry and Sanofi hadn't been able to solve a problem. Maybe it couldn't be drugged, maybe it had a serious side effect, maybe it wasn't discovered and it's a first-in-class. And those 5 milestones all say, we think we got it. And now we have to go through some additional testing to make sure it would be a good drug to take into patients, at which point it hits the development candidate. The nice thing about that development candidate milestone, it ends our operational obligations. So that's all profit. They're also large milestones, but that's all profit that drops to the bottom line. And as you mentioned, we're able to do that across 15 programs. And so we love to see that continue to grow and expand. With Roche, where that started, it was originally to try and come up with new ideas in neuroscience. And so the $60 million in milestones that they paid us were actually doing first-of-the-kind maps of different neurological cells that allow us to think about the biology of neurological targets in a different way. And so that was really exciting to see. We got an initial payment from them for $150 million upfront and then bringing in those $60 million of milestones for the success on the maps. What we'd like to do there is now start converting that over into programs, which then looks more like Sanofi. And so when you think about those programs, the average milestones are about $300 million over the course of the program. Most of that, so for Sanofi, for example, $193 million of that is pre-commercial. So this isn't some big back-end [ biobucks ] loaded milestone pack and spread relatively evenly. And then average royalties are sort of high single, low double digits. So really nice economics behind all of that. So between the ability to do that in a capital-efficient way and really the high value of NPV that we capture, we love moving those forward. I'll say whenever you're in a partnership, it's going to go along with the partnership as well. So there is a different risk profile to it. There's a different flow to it versus our internal pipeline, which is why we always wanted to keep the balance between the 2. If you look at our internal pipeline, like we can talk about the targets. We can do that with a partner pipeline. We can control the gating and the milestones associated with that. It's much more standardized in some of the partnership side. And so what we try and do is balance our risk profile and diversification across both of them.

Back to your internal pipeline. Maybe talk about the various readouts that you have upcoming over the next 12 to 24 months. What excites you guys? And what should we -- what should the investment community be on the lookout for?

Yes. Really, really exciting time for it. So we just had positive proof-of-concept readout on our most advanced program, which is in an orphan drug indication called FAP. And that we saw near 50% responses after only 3 months. It was durable even off drug, 50,000 patients in the U.S. and EU5 with no therapy that's approved for it. So this is a really exciting area for us to keep going ahead. We are clarifying with the FDA the pathway for the pivotal trial there. And then we'll have additional data coming out on that first half of next year in addition to getting the pivotal trial started. On our other programs, so we've got 4 other clinical programs. All of them have data between now and first half of next year. And most of those programs, so they all have -- well, the right way to put it is, they all have different aspects that had not been solved before that our platform has addressed. And in sort of the preclinical work and sort of the laboratory work, it looks like we've found ways to overcome those obstacles, whatever they be biological or chemical. And so this clinical data coming up will show did -- not only did the platform do what it was supposed to do and resolve those problems, but also does that make it a good potential drug. And so a lot of proof points coming up in that time period.

Sorry, just trying to unmute myself. I think an underappreciated part of your platform is the molecule patient side, the AI-powered clinical trial design and patient recruitment ability. Maybe walk us through those, Ben, and how you're seeing traction with pharma customers on that part of your platform? I think most people don't realize you have this as part of your platform.

Yes, you're totally right. So this is where it's funny because we have all of the infrastructure you need to be able to handle data in an AI type of way, be able to create and train models. So we have our own internal supercomputer and then the expertise to be able to do all sorts of different modeling systems. And so what we did is we basically in-licensed real-world data from a variety of different data providers. So this is going to be actual patient data, whether it's coming from clinical trials or claims or whatever other source. And we use more than 6 different data providers to get the information we need. But then what we can do is create modeling systems to say 4 patients that have this gene mutation or 4 patients that have this disease and this comorbidity, who's more likely to respond? Or we can also look at it and say, where are they? Like one example we gave is for the FAP program, being able to target in and identify here is where there are a couple of dozen patients. It's not a normal clinical trial site. And this is one that we should be at, right? And we were able to see enrollment rates increase by 30% to 60% on the trials that we're using this on because we're just being smarter about how we look at patient populations and we look at targeting rather than just relying on sort of the traditional methods that we've seen out of the CROs and some of our partners. So if you break it down, $0.70 out of the dollar in drug development and discovery is spent on clinical trials. Those clinical trials are all based on statistics. Like we talk about biology, we talk about chemistry, we talk about a bunch of different things. In the end, it's what do I statistically need to do to prove that this drug works or doesn't. And so the better that you can identify the right patient population, the better that you can design your trial, the better the statistical plan will be, which means you will prove that out with fewer patients. And that means efficiency on the clinical trial time and cost associated with running that trial. And so we apply it to everything that we can to understand the patient, understand the clinical environment, understand the sites. And what we're seeing is improvements that are having real financial and timing impacts to our clinical trials. Our -- you would think that everyone would be doing this, but the fact is that they're not. And so we've had a lot of interest out of our partners and trying to help them think through these questions as well.

Yes. I mean it totally makes sense. I guess taking a step back here, Ben, maybe talk about your balance sheet, capital on hand, what you have -- how long you have to cash burn for these clinical programs? Yes, walk us through all that, though.

Cool. So we ended the year with $754 million in cash, and that gives us a runway out into early 2028. This has been a really core focus point. So the companies independently were spending over $600 million a year when we brought them together in 2024. And we took just in the first 12 months, over $200 million out of that budget while still substantially expanding our capabilities. So how we did that? We talked a little bit about sort of looking at the pipeline and thinking about what has the most impact. But honestly, we did that across the entire company. So right now, 2/3 of our budget is focused unapplied, so whether it's experimental or technology or anything else, this is applied costs going into pipeline and partnerships. So we don't have a massive part of our budget that is going towards sort of blue sky research or unapplied experimentation and tech development. We actually believe that the best way to develop good technology is actually to have a program to develop it on because what you end up doing is you design something, you know what it's supposed to do. You also create an experimental validation for it and then you learn really fast. So rather than sort of trying to make and train a model in a vacuum, we're able to actually like say, is this working now or not in the real world? And what we've seen is over and over again, we can get a model to work really efficiently and bring it up to speed. And that's why we've been able to take 90% of the experimental cost and work out of chemical development, and we've been able to cut the time lines from idea to to identifying the right compound in more than half, right? Like we're doing it in 17 months on average versus 42 months as the industry standard. So we're seeing massive benefits while we still build pipeline or platform value by doing it in applied way. Long story to come back to, that is actually a really efficient way to run a budget as well. And we keep taking money out of the budget in the sense of we figure out ways to do it better, faster and cheaper. We're a tech company. Even if we're doing biotech, we are a tech at heart. And so we need to be focused on that efficiency because almost always, efficiency actually means quality. It means you're getting to the right answer quickly and with the best possible system.

Last question for me, Ben. Where do you see Recursion in 5 years from now? Is it just a larger version of its current platform where you have more partnerships and more pipeline? You walk us through -- walk me through the vision of Recursion.

Yes. Look, I mean, I don't want to -- so the AI industry is so full of hype. We really try and not jump into that or trying not to have that be sort of the lead. I think we do have big ambitions, but what we want to do is really prove it step by step. Is our technology scalable? Absolutely. Should you scale it before validation? No. We are already partially scaled, right? We're running over 20 programs internally, which is massive for a company of our size. If we have success across our clinical pipeline and partnerships, would you imagine that scaling further? Sure. Now what I will say is we want to manage that with the right budget and infrastructure. So if we -- if all 5 of our clinical programs went well, which, by the way, we never guide to that. We always assume that there will be some failures in there. But if they all went well, we would probably not try and take them all forward ourselves, right, because that is a really big infrastructure and clinical commitment to do. Could we commercialize products on our own? Absolutely. We have some people with expertise in that. One of the factors that we looked at our pipeline with was, is this something internally that we can advance ourselves if we decide to. They had to have that in because we never wanted to be, "hey, we developed this great product and we can't take it forward because we need a partner." So we were really able to focus on that. But I'd say the focus of the management team, where we want to set expectations is look towards the near term. We've got a ton of value potential coming up. We're going to do it in a portfolio management strategy of killing early the things that don't work and investing behind the things that do.

Perfect. Well said. Well, thank you so much, Ben, for doing this fireside chat with me. Investors, if you have any follow-ups, please don't hesitate to reach out to me directly, and I can put you in touch with Ben. But thanks again, Ben.

Terrific. Thanks a lot, Scott. Have a great night.