All right, good morning again, for I see some familiar face from the last one, but thank you for sticking around. It's good to see everybody, and welcome back to TD Cowen's 46th Annual Health Care Conference . It is my pleasure to be joined today by Recursion CFO, Ben Taylor. We've got a lot of great stuff to unpack, discuss, sift through. You know, we have our own questions, by any means, by all means, excuse me, if you've got any questions, just kinda flag me or feel free to send me an email at brendan.smith@tdsecurities.com. I'll be checking my phone. Ben, maybe let's just kind of start by taking a quick second to update us on really the, you know, Recursion platform of, you know, March 2026.
You know, where you kind of see most important areas of evolution recently and what this kind of tells the rest of us about what the platform's actually able to do today that maybe it hasn't been in years past?
Sure, of course. I think there are a couple of different pieces that come together. We have obviously changed the platform and the business model quite a bit over the last 18 months. What I think that really reflects is going from a single-point solution- focused company or companies to really trying to create a integrated platform as well as an integrated business model. What I mean by that, maybe starting with the platform first, Recursion a couple years ago was really focused on inductive phenotypic screening to uncover novel biological targets. Really, how do you translate that novel discovery into a clinical program and in the future in medicine?
What you've seen now in our platform is we've taken that novel biology, and we've added on transcriptomics to it, we've added on proteomics to it. We do a lot of reverse genetics using real-world evidence. We've also added in through where I came from on the Exscientia side, a novel design platform that's really looking at how do I take that nearly infinite chemical space and try and come up with a good chemistry to try and test that biological thesis and try and solve problems where the industry hasn't solved it before. We added on to that as well, and this was really thanks to our new CEO, Najat Khan, who had joined from J&J after overseeing their portfolio strategy as well as developing a lot of their AI technologies.
How do you look at the clinical side of the world, the real-world evidence? How do you design better trials? Also using that to bring back into the biology and chemistry and thinking through what's the right patient and how should I design this for the right situation. What you've seen is we've come together, and that's become a single workflow across the company, where we're saying, "What can I use to better understand the biology, the patient, the chemistry, the clinical trial I need to run, so that I'm incorporating better predictive modeling so that I hopefully have a lower failure rate in the clinic?" That's been how the technology platform has come together. At that same time, we've also been really trying to do the same with our business model.
This is something that's very different in what we're doing today. If you look at our partnership business, we've crossed the $500 million mark in inflows coming into the company. That's really making a dramatic difference in how we're able to build and invest in the platform as well as a great contributor to the NPV. Also on our internal pipeline, we've got five clinical programs, two preclinical that we own the rights to. That's not in a single indication. It's not in a single technology. It's not a single scientific principle. What we try and do is say some of that'll be first- in- disease or first- in- class, some of it'll be best- in- class.
We really wanna take a risk-diversified approach to how we're advancing that. This is coming back to our original mandate as a company, was actually to take some of the binary risk out of biotech and make it into more of a business model. That's really starting to come together in a new way.
Okay, great. I know you mentioned Najat. We've spoken with her a number of times. It's great to kinda see the company, I would say 2.0 under her leadership. Kind of tied to the evolution of the platform, I guess what are the most important kinda things to flag from a strategy leadership standpoint in the Najat era now, and maybe kind of tied into that, how we should think about even expenses, right? Like cash burn kinda tied into all of this. As we look at your growing and expanding pipeline, how is Najat kind of approaching what Recursion is able to do with that pipeline through the lens of kind of burn moving forward?
Sure. Well, I'll start answering in reverse there. Everything that I just talked about, we did last year for about $400 million in gross spend, not including any of the inflows from the partnership. The comparison in pro forma 2024, that was $606 million. We took $200 million out of the expense base while actually really expanding our capabilities and building out our clinical pipeline. I think those two, the previous answer and this one, is a great analogy to what Najat Khan's focus on the business is. How do we get down to those things that matter most and be disciplined about making decisions around them? Not to invest in areas that have a low potential return, not to invest in things that don't have a clear clinical or commercial pathway.
Like, we have so many things that we could do. How do we focus on the things that we will do best and that we can have confidence this is a bet that we should be making? That's been something that she's really come in. Obviously, she's got fantastic operational and scientific experience, and so really bringing the rigor into understanding how we're making our scientific decisions, what we're investing in on the technology side. She's both a coder and a chemist by background. That's been really a fact-based, data-driven, disciplined management style.
Okay. You mentioned some of the partnerships now too, and I think this is something we are kind of constantly trying to unpack on our side, both as analysts trying to actually build out some of these models and understand when and where the value is coming from. Also I think on the investor side too, trying to get at, you know, really what is differentiated about, like what Recursion is able to do versus, you know, some of the other AI players in the space when you're talking to some of these pharma partnerships. Maybe we can use the Roche PhenoMaps conversation-
Sure.
As a, as an example here. I know you recently announced a, kind of another milestone within that. Can you maybe just walk us through, first of all, kind of just the economics of that particular partnerships and that particular partnership and how representative it is of, you know, Recursion's ability to kind of work with pharma? But then also again, maybe extrapolate a little bit to what you're hearing in your conversations with pharma, with whether that's Roche or others, and how we should kind about the capacity to partner with AI companies?
Absolutely. We get this question a lot because obviously you see things like Lilly making very large investments into the AI space, and as well as a number of other companies. I mean, Roche and Sanofi, who are our two largest partners, have obviously made a lot of investments themselves. They continue to invest behind our partnerships. With Roche, in particular, to date, we've brought in $210 million from that partnership, largely around the construction of novel PhenoMaps towards neuroscience. This is looking at cellular systems, transcriptomics, whatever other data sources we can create a map from to really understand what are some novel targets in neuroscience. This has been a corner of the industry that has had very, very few new targets over the last several decades.
What we're finding is there are a lot of new potential targets that are coming up when you take data creation differently and then look at it differently. That's been the collaboration with Roche, where we've had the $60 million in milestone payments coming in from those two maps. Right now, we're really looking at how do we convert some of those ideas into design programs, so actual work on creating the medicines from those ideas. That has continued to move ahead very, very well from both sides.
I think what's interesting, how we structure our partnerships is really we try and get paid in advance for the direct costs associated with our development of the technology and/or program, and then maybe have an early milestone, like with our Sanofi collaboration. We have now hit five discovery milestones, what we're really doing there is each one of those programs is an example of there was something along that target that had not been solved by industry before. This is a collaboration with Sanofi to say, "Can we do something better? Can we get to a target product profile that no one's ever reached before?" The initial discovery milestone looks at it and says, "We believe we've got there.
We have to verify that it's a drug that's gonna move ahead. That's the next milestone with each of them. What those collaborations, whether it's Roche or Sanofi, lead to is once we've completed our aspect, which hopefully either comes up with a new target, and/or a new molecule, they would bring it in. On Sanofi, those five programs, the next milestone is Development Candidate . That is effectively profit coming towards us because it ends our operational obligations, Sanofi carries forward, and does the clinical development moving ahead. With Roche, what we're doing is converting those ideas into the design programs, which would follow a similar trajectory. Getting back to your original question of what does this mean for the industry? How is this differentiated?
Work with us because we're not only creating great models. There are actually a lot of companies out there who can create great models. What you need to be able to do, you need to have a data set that actually powers it. you know, we've got tens of petabytes, over 50 PB worth of our own proprietary data that has been created by us in a way that is usable for machine learning and other AI techniques. That's so important because most of the public data out there is, it's effectively dirty data. It's very hard to use. You need to do a lot of cleaning before you could use it for machine learning, and it's also going to point you towards those same things that have already been drilled into.
One important thing a lot of people forget about, right now, about 3% of the genome has an approved drug for it. If you look at everything that's in development, you're gonna be closer to about 10%. That's a tiny part of biology that we're playing around with. Almost all of the data that's in existence is focused on that part where there's already been something developed. If we want to start to address that other 90% that's still basically a blank sheet, we need different techniques to go into it. This is where our data creation, our models, our being able to do this on an integrated basis makes such a difference. Because any one model system that you create is going to be messy. There's going to be a lot of noise.
One of the things that we found in that integration that we talked about earlier is being able to take multiple different modeling systems and compare them to each other, you can start to take some of that noise out. The only other way to test it is experimental. What you actually want to do is create a great new novel modeling system, have other modeling systems that could actually help you say, "Is this noise or is this real signal?" Then as late as you can, get to that experimental validation.
Can I ask actually just maybe one last one here before we dive into the pipeline itself, how this answer shifts if instead of me asking about pharma, I'm asking about the big tech players. Obviously, NVIDIA made some waves, past couple of weeks here. How should we kind of think about their impact on AI drug discovery players such as Recursion? like where is their interest in all of this? You're mentioning a lot of kind of recognition that a lot of the data that's sitting out there is dirty or maybe not as useful as maybe a lot of us on the outside would think just based on the sheer quantity that's available.
Is a lot of this kind of identifying what's good and what's not, building out some of the model systems to be able to fill in some of those gaps? How does the answer shift when we're talking about an NVIDIA or an AMD versus-
Sure.
Their healthcare initiatives versus like a Sanofi or a Lilly?
Yeah. Actually everything that they're doing, whether it's NVIDIA or Anthropic or different groups, in the end it's very helpful to us because a lot of what they're designing for is actually either workflows or greater compute capability. If you, if you break it down, the underlying modeling systems still need independent creation and validation. That workflow that goes along the top, and this is, you know, all of the agents and the different aspects like that that are being built, are basically as good as the foundations that you put them on top of. You need that foundation. We look at some of the work that's going on there and fully embrace it and bring it in as quickly as we can because it just makes our job easier.
You still have to create those underlying systems. I think NVIDIA is a great one. I'm sure everyone saw some news. It was sad to see them go as an investor, it was actually completely separate from our corporate partnership. We have an ongoing corporate partnership with NVIDIA that's doing great. They shifted their portfolio strategy and are obviously focused on quite large investments right now. We continue to be a really good partner. They, we have one of the fastest, I think it's technically still the fastest, though Lilly will take it, supercomputer in biopharma. That was all a NVIDIA collaboration, we continue to build on top of that. That compute is essential to what we're able to do.
I mean, the fact that we have it in-house means not only, can we do it faster and we have a lot of cost savings, but every incremental improvement that they can make onto that, all of a sudden it amplifies our ability to use their modeling systems to search the data to really power how this all works together.
All right. I wanna now dive into the pipeline itself because there's a lot going on here. Maybe let's start with REC-4818 in FAP, right? You recently put out 25-week follow-up data as is the MEK1/2 inhibitor. I guess first, really quickly, how did that initial update last year kind of measure up to what you were hoping to see also in the context of the current standard of care, and really now lay out for us what are the next steps for this program over the coming months?
I think the data itself was about as good as we had hoped. We saw dramatic reductions in polyp count. This is a disease where because of a genetic abnormality in the patient that they were born with, they will continue to have cancerous polyps grow in hundreds or even thousands of numbers throughout their intestinal tract. What you're trying to do is control that polyp growth, because as the physicians look at it, if the polyps look like they're becoming cancerous, that's when they're gonna do resections. What our goal is to minimize that impact to patients, not only try and control the polyp count and burden, but also limit the need for resections and other surgeries for these patients.
We saw a more dramatic reduction in polyp count than it's ever been seen in the area. Also, we had the patients, and we took them off drug for a three-month period. What we saw is those responses were stable over that period, which is a really massive statement because this is a drug that in an ideal situation you'd be giving to the patient chronically for their life. You wanna see not only that mechanistic action, but also the durability with it. What we are looking at now is we're engaging with the FDA to figure out what the right next steps are.
I think the big question mark coming out of the data is just what does the regulatory path look like? Because this is a potential first-in-disease drug, we're engaging with the FDA to find out what the right trial design will look like going forward. We also did a large natural history study, getting back to some of the things on how we can make a difference. We did two, actually. One that was following patients to see, do these polyps naturally go away? They don't. The other question, we were actually able to create a foundation model within a couple of days that looked at about 250,000 clinical records and said, "How are these patients actually being treated? What are the doctors writing in the notes about these patients?
What is the actual standard of care?" Nobody knew, right? This is not something where you've actually got a stable standard of care. That was incredibly important in us being able to look at what are these patients likely to see in the clinic? What's their experience gonna be? What other drugs are they gonna have? All of those different aspects.
Presumably now the update from these conversations with the FDA in the first half would give you a sense of when you could potentially start a pivotal study and what that study would actually look like.
Mm-hmm.
-more or less. Okay. I guess kind of tied to this, because I know you all have talked about, the evolution of the ClinTech platform within, you know, Recursion platform itself, is it fair to assume that a lot of those investments when you're looking at starting a pivotal study for FAP, does that mean that you could find those patients faster, enroll them faster, and maybe get to an actual pivotal data that much sooner? I guess, what's that kind of look like through the lens of Recursion today?
Well, we definitely hope so. We started to put out data. ClinTech is a relatively new platform. We basically created it over the last year for us, we've already started to see results. What we've seen is enrollment going about 30%-50% faster than the baseline that we had previously, which is really exciting, that's basically, we gave an example of this in our earnings, we're able to dive in and understand every one of the clinical sites, the patient populations, and basically design our sites and design our outreach to be able to reach those ones that have the most potential to get us better patient populations.
This is something you would think you would do along with the CROs, but I can tell you now, having been involved in it many times over, it doesn't usually happen. The way that we're doing it is actually quite different. That's one of the ways that we're driving it. The other is being able to say, who are the actual patients who I think are going to respond best out of it? That's really looking in, and that can actually lead us down different indication pathways. We gave this example with our CDK7 molecule, where if you talk to any of the KOLs, if you look at any of the papers, everything drives you towards estrogen receptor-positive, HER2-negative breast cancer, generally in a CDK4/6 refractory area.
We said, "Okay, fine. The preclinical data supports that that should work, but what else could we do?" We looked at different patient populations. We looked at different CDK7 mutational profiles for real-world patients and how that affected them in different indications. What we found is actually it made a pronounced difference in ovarian cancer. This is really interesting for a couple reasons. One, there are a number of ovarian cancer patients who are also estrogen receptor-positive, so maybe there's a potential link to why that could be more successful in breast cancer. Also, this is from our own anecdotal data, and I consider all phase I trials anecdotal.
From our own anecdotal data, we saw a partial durable response in a fourth-line metastatic ovarian cancer patient on monotherapy in our own trial. We didn't expect to see any responses because this is effectively a cytostatic mechanism rather than cytotoxic. It's a cell cycle disruptor and transcription inhibitor. That was really exciting. We were seeing it from multiple different angles. There's a logical mechanistic angle. There's a real-world inductive evidence that it should have an impact, and we saw something coming across anecdotally in our clinical trial. We pivoted the clinical program to ovarian cancer. There are other indications that we could go after and expand into, we felt like that was a good place to start.
I wanna ask a little bit more about the CDK7 drug. To follow up, the 30%-50% faster enrollment that you mentioned, is that in one specific drug, one specific program, or is that kind of just across the board?
No.
I guess or I'm curious, like, 30%-50% faster than what's the comparator here? Like, what you had been seeing in your own trials before you did this investment or what you've seen kind of broadly in each of these spaces?
Yeah. Multiple different trials. Both is the answer on the what's the comparator. We looked at basically when you work with a CRO, they will give you an estimate of what they believe is likely to happen. We also do our own work and look at historical clinical trials and look at the enrollment rates from historical clinical trials. We looked at the trials that were running before and after we implemented the change. It's the exact same signal across all of them.
Okay.
That range, the 30%-50%, is probably three or four trials.
Got it. Well, you know I love those kinds of metrics.
Mm-hmm.
It's nice to see-
Yeah, we'll have more coming.
More time. Great.
We'll have more coming.
Great. Okay, on CDK7, you know, when we're talking about ovarian cancer population, I guess, how does the data you've seen so far now, and you talked a little bit about moving into this, kind of set you up for what is the timeline now for?
Mm-hmm.
For this program and when we can kind of expect, you know, next data readouts here, and how has data you've seen so far kind of informed where it makes the most sense to go forward?
Sure. CDK7, what we've done there, and I mentioned about the ovarian cancer, this is a drug that'll almost always be used in combination. That's true of most any oncology indication now. We ran the monotherapy and put out the data most recently in December. What we wanted to do is then start the combination dose escalation and then expansion, the phase I/II basically in combination. That is ongoing now. The initial focus is on ovarian cancer. It is designed as a basket trial that could be expanded into other indications as well. Our guidance for data is first half of 2027 for initial data coming out of that.
That'll be, you know, it's one of those high-risk, high-reward, but would be really exciting to see the impact coming in. CDK4/6 is a $9 billion-plus commercial market right now. We think that the opportunity for CDK7 is actually a lot broader.
Right. I guess this kind of gets at, something I was asking earlier, but when we're talking about. You have the CDK7 update coming first half of next year. How are, you know, beyond FAP and CDK7, the rest of the pipeline now I know Najat has spoken a little bit more to framing some of these updates as kind of go/no-go decisions, right?
Mm-hmm.
I think for a lot of us, you know, that it feels like, okay, there's clearly a bar internally you wanna see, and if it's not met, then you kind of move on. How is kind of that reframing of some of the earlier stage pipeline impacting, like, some of the strategic burn per asset...
Mm-hmm.
For example, like, as we kind of look at the next 18-ish months for Recursion, like what are kind of the most important pivot points within the development of the pipeline through that lens?
Yeah. Well, this gets back to the very beginning. Everything we have has a near-term go/no-go, and that will be data-driven. There is no drug that we feel like, "Hey, we're comfortable with really gray areas here." That's an important distinction because there is no single drug or partnership that makes up the value of our company. We look at this as a portfolio management approach. Every drug that we have has a reason that comes out of our platform that we think it has a better probability of success, and so that's why it's there. If we don't see the data supporting what we want, it'll be terminated tomorrow. Even though we brought 35% out of our expense base over the last year, it's not enough. It's still a high burn.
If all seven of the drugs that we've talked about in our pipeline are successful, I think that drives you to not, oh, how do you figure out how to manage all of that burn? You probably just need to start to do out-licensing or different activities like that. What we have right now is a business model with a lot of option value in it. We need to finish running the current experiments that are ongoing, but if any point we see negative data, those drugs are gone. That's something that we will hold to. We hope investors watch and hold us to it as well.
Okay, we've got the update for FAP first half of this year, excuse me. We've got the CDK7 data first half of next year. We'll also see some updates on EXS-73565, if I'm not mistaken, and 102 in the second half of this year, if that's right.
Yep.
Maybe talk us through what the go/no-go for those programs look like, and where you kind of see the bar, like we need to see success on this metric, this metric for each of those. Otherwise, we need to kind of reinvest in the ones that we have more confidence in?
All of them need to realistically exceed traditional benchmarks for your safety, your PK, your PD, you know, whatever it is that we're looking at at that particular stage. However, I'd say if you look at like MALT1, that was specifically designed to take UGT1A1 inhibition, which causes hyperbilirubinemia out because you're gonna use that drug with a BCL-2 or a BTK inhibitor, which are gonna have liver tox. You have to take that out. All of the drugs that have been developed for it have that. Not only does it have to look like a really good drug, you have to get rid of that. LSD1, similar in the sense of, we've seen mechanistic effect from it, but everything in that class causes a lot of thrombocytopenia. If we see a lot of thrombocytopenia, it's gone, right?
That is an on-target effect, to be clear. That's what we're trying to use our system to better develop, is how can you actually minimize the on-target side effects by designing a better drug? Without going through every program, each one has something specific in addition to the fact that it has to be a good-looking drug.
Got it. Okay. I know we're basically at time here. Maybe last question. You know, we covered a lot of the pipeline, we've covered the burn, we've covered the renewed strategy here. Where are you kind of now as you look at the Recursion of 2026? Where are you feeling like, the biggest source of disconnect is between where a lot of investors, a lot of analysts are trying to understand the value for Recursion and where ultimately you see a lot of the day-to-day value being driven?
I think there's a couple of parts. I think a lot of people still think of us as sort of a single-point solution company. We still get people asking us about, you know, how are the, you know, the phenomics programs doing, which of course we are focused on every part of it, but it's a corner of the company versus where the company is now. I think that's lost. I think the value of the partnerships, I mean, if we were a smaller biotech, each one of those programs were probably something that you'd see on our pipeline with a logo of Sanofi next to it or whatever, right? They're really fantastic programs. We can't talk as much about them because they're partners, but they're fantastic programs and we've got great economics.
I think understanding the risk diversification that we have across our pipeline and platform, and understanding our partnerships.
I think with that, we are at time. Ben, always great to see you. Thank you everyone for listening in. Got a lot more to come. Great.
Thank you.