Great. Thanks everyone for joining. My name is Scott Sc hoenhaus. I am the healthcare tech equity analyst here at KeyBank. 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. You've gone through a lot of changes over the last 12-24 months too, so it's worth highlighting your story to anyone that's ne w to it.
Yeah, of course. Happy to. My own background, I was actually coming over from the Exscientia side of the merger. I was the CFO and Chief Strategy Officer over at Exscientia, and had been there for a little over four years, when we brought the companies together, 18 months ago. Before that, I had actually run the day-to-day operations at an oncology biotech. 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. 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. I'd been in ba nking before that.
A lot of my clients had really been guessing because the data that you have to make good decisions is so sparse. We'll take, you know, data that's coming out of a few animal models or a few unrelated studies and try and make a guess as to, you know, what's going to be a successful trial or drug or patient population. 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 had been working on doing that in the chemistry space. A lot of people focus on the biology component of biotech.
In fact, once you think you have an idea for a good target, a lot of trials actually fail because of the chemistry side of it, because either, you know, everybody knows about the potency or selectivity, so are you hitting your drug and your target and not hitting other ones? There's so many other properties that go into it being absorbed and how is it metabolized and the toxicity profile of it. That's something that AI really enabled us to do a much better job of doing a multi-parameter optimization. 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%. That means about 90% of biology is effectively unexplored. Even the 10-ish% 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. 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. That's where we've built our phenomic system. We do a lot of it with transcriptomics. We do a lot of real patient data. Then how do you create a better drug for it?
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. This is where we've been able to increase our enrollment rates by, you know, 30%-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 trials. We've been able to put those three pieces together, and I think that's been.
Oftentimes we're asked, "What's your differentiator?" I think classically it was always, you know, 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. I think over the last year and a half, 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. We're not a single point solution.
That's really helpful for that context. Ben, I guess maybe, you know, 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 all together? How do you think about advancing certain other molecules, like REC-617?
Yeah.
Walk us through that strategy.
Sure, of course. When we came together, we had a massive pipeline of about 10 clinical agents and far more than that on the preclinical level. That was just too much for us as a single company to take forward. We do have some large partnerships. We brought in over $500 million from our partners on that.
Just focusing on our own internal pipeline, we said we need to be really focused and bring the highest impact programs forward. What we did is ground up, you know, 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 gonna 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. 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 five in the clinic that we have taken forward.
We've got about 15 programs in discovery between our own and our partner programs. 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 milestone payment, right? 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?
Yeah.
Yeah.
Yeah. We love our partnership business. Our main partners are Sanofi and Roche. We do also have smaller partnerships with Bayer and Merck KGaA. As you brought up with Sanofi, we'd hit the initial milestones on five 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. Those five milestones all say, "We think we got it." Now we have to go through some additional testing to make sure it'd 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. As you mentioned, you know, 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. The $60 million in milestones that they paid us were actually doing first of their kind, maps of different neurological cells that allow us to think about the biology of neurological targets in a different way. That was really exciting to see.
We got an initial payment from them for $150 million up front, and then bringing in those $60 million in 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. When you think about those programs, the average milestones are about $300 million over the course of the program, most of that. For Sanofi, for example, $193 million of that is pre-commercial. This isn't some big back-end biobucks-loaded milestone pack, and spread relatively evenly. Average royalties are sort of high single, low double digits. Really nice economics behind all of that.
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, you know, whenever you're in a partnership, it's gonna go along with the partnership as well. There's 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 two. If you look at our internal pipeline, like we can talk about the targets. We can't do that with a partner pipeline. We can control the gating and the milestones associated with that. You know, it's much more standardized in some of the partnership side.
What we try and do is balance our risk profile and diversification across both of them.
Back to your internal pipelines, maybe talk about the various readouts that you have upcoming over the next 12-24 months. What excites you guys, and wh
at should the investment community be on the lookout for?
Yeah. Really exciting time, Fred. We just had positive proof of concept readout on our most advanced program, which is an orphan drug indication called FAP. In that we saw, you know, near 50% responses after only three months. It was durable, even off drug. You know, 50,000 patients in the US and EU5 with, you know, no therapy that's approved for it. 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, we've got four other clinical programs.
All of them have data between now and first half of next year. Most of those programs, the right way to put it is, they all have different aspects that had not been solved before that our platform has addressed. 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. This clinical data coming up will show, 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. A lot of proof points coming up in that time period.
Sorry, I was trying to unmute myself. You know, I think an underappreciated part of your platform is the molecular patient side, you know, the AI-powered clinical trial design and patient recruitment abilities.
Yeah.
Maybe walk us through those, Ben.
Yeah
how you're seeing traction with pharma customers on that part of your platform. I think most people don't even realize you have this as part of your platform.
Yeah, you're totally right. 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. What we did is we basically in-licensed real-world data from a variety of different data providers. This is gonna be actual patient data, whether it's coming from clinical trials or claims or whatever other source. We use, you know, more than six different data providers to get the information we need.
What we can do is create modeling systems to say, "For patients that have this gene mutation or for 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, you know, here is where there are a couple dozen patients. It's not a normal clinical trial site, and this is one that we should be at, right?
We were able to see enrollment rates increase by 30%-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. If you break it down, 70 cents 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?
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. That means efficiency on the clinical trial time and cost associated with running that trial. We apply it to everything that we can to understand the patient, understand the clinical environment, understand the sites. What we're seeing is improvements that are having real financial and timing impacts to our clinical trials. You would think that everyone would be doing this, but the fact is that they're not. We've had a lot of interest out of our partners and trying to help them think through these questions as well.
Yeah, I mean, it totally makes sense. I guess taking a step back here, Ben, maybe talk about your balance sheet, capital on hand.
Yep.
You know, what you have, how long you have to, you know, the cash burn for these clinical programs. Yeah, walk us through all that, so.
Cool. 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. The companies independently were spending over $600 million a year when we brought them together in 2024. We took just in the first 12 months over $200 million out of that budget while still substantially expanding our capabilities. How we did that, we talked a little bit about sort of looking at the pipeline and thinking about what has the most impact. Honestly, we did that across the entire company. Right now, two-thirds of our budget is focused on applied. Whether it's experimental or technology or anything else, this is applied costs going into pipeline and partnerships.
We don't have, you know, a massive part of our budget that is going towards sort of blue sky research or unapplied, you know, 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. 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?" What we've seen is over and over again, we can get a model to work really efficiently and bring it up to speed.
That's why, you know, we've been able to take 90% of the experimental cost and work out of chemical development. You know, we've been able to cut the timelines from idea 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. We're seeing massive benefits while we still build pipeline or platform value by doing it in an applied way. Long story to come back to, that is actually a really efficient way to run a budget as well. 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, you know, 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 from me, Ben. Where do you see Recursion in five years from now? Is it just a larger version of its current platform where you have more partnerships and more pipeline? Walk me through the vision of Recursion.
Yeah. Look, I mean, 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 wanna do is really prove it step by step. Is our technology scalable? Absolutely. Should you scale it before validation? No. You know, we are already partially scaled, right? We're running over 20 programs internally, which is, you know, 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 wanna manage that with the right budget and infrastructure.
If all five 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. 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." We were really able to focus on that.
I'd say the focus of the management team, where we wanna set expectations is, you know, look towards the near term. We've got a ton of value potential coming up. We're gonna 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. Thanks again, Ben.
Terrific. Thanks a lot, Scott. Have a great night.