Good afternoon, everyone, and thank you for joining us on the first day of Needham & Company's Healthcare Conference. My name is Gil Blum, and I'm a Senior Biotech Analyst here at Needham & Company. It is my pleasure to have with me today Recursion's Chief Financial Officer, Ben Taylor. As a reminder, any viewers who are watching through our conference portal are able to ask questions via the Ask a Question box below the video feed window. Ben, maybe just starting with a bit of an introduction by setting the stage and just discussing some of the company's priorities in 2026.
Sure. Well, thanks, Gil, and we really appreciate you having us at the conference. 2026 has been, it's really, if you think about it. If last year was transition because we had the merger that happened towards the end of 2024, and so we were doing a lot of reshaping the strategy, reshaping the financial structure, putting together the right pipeline. Now we've started to enter into that phase in 2026, where we're looking at data readouts and partnership milestones on a more and more regular basis. We already had our first proof of concept data readout with our FAP molecule and program moving forward, and now we're in FDA discussions on that. We've got four more clinical programs that are moving in behind that.
Also our partnership business, where we've already brought in over $500 million through our partnerships, and moved five candidates forward in early discovery with Sanofi, brought in over $60 million from Map milestones with Roche, we want to see a more regular cadence of all of that coming through. On that backdrop of data, we also have a lot going on with the platform. We've built out Recursion historically, was always focused on phenotypic discovery. On that biology discovery, we've built in transcriptomics, we've added in using real-world patient data, and really are focused on that multimodal integration to be able to do better biology discovery. Obviously, bringing in Exscientia changed the profile of being able to do chemical design and now we can discover the targets as well as build the compounds.
Also quietly, and you were one of the first to notice it, Gil, quietly in the background, we've also built up a pretty robust ClinTech business that's allowing us to design better programs, understand the patients better. We've seen real data where we're improving enrollment by 30%-60%. We're being able to identify sites in a better way and really understand the patient populations in a better way.
Well, the way that we viewed the business model at Recursion included three major components, the internal pipeline, the pharma partnership, and maybe the Recursion OS itself. Can you rank these components as it relates to your value creation? Who's your favorite child?
Well, it's funny. Our original mandate as a company was figure out how you can make a more risk-diversified way of advancing biopharma. And a lot of our original investors, not only did they embrace technology, but they didn't like the classical model of biotech investing, where you have this binary risk. And so that's why we have the diversified business model that we have and different ways of bringing it in. And even inside of that, if you look at our internal pipeline, some are more focused on the biology, some are more focused on the chemistry, or both. We didn't want anything to really be limiting us. Now, if you look forward, I think the partnership business obviously can be more of a steady rolling business than the biotech, which is very up and down based on data, the internal pipeline part of it.
They also have different value propositions. If you look at the biotech industry as a whole, good proof of concept clinical data can be absolutely transformative, and it's much harder to do that with parts of the partnership business. We want both. We want to be able to build our platform and bring in cash inflows through the partnerships. That is scale and capabilities that would exist regardless of if we had a partnership business or not. Why not leverage our ability to do things faster and better and cheaper to build out a partnership business, rather than just focus it on our internal pipeline?
Another key differentiator, at least in our view, for Recursion, has been the ability to industrialize data generation at the discovery stage. Are you seeing some of your competitors, pharma? Is anyone doing this kind of investment these days? Things have changed in the last year, I would say.
Yeah, absolutely. Well, this is funny. This is why I think if you look at the NVIDIAs and the Googles of the world, the reason they're such good operating partners for us is if the entire industry did business in the way that we do, it would absolutely be one of their largest segments. What we expect is that the rest of the industry will start to adopt it more and more, because we are just showing that it can be done not only faster and cheaper, but really to get to results that hadn't been achieved before. Eli Lilly is the most notable party stepping into that and investing some of their good GOP money into building out the business. I think you'll see a lot more.
The reason you'll see a lot more is if you take all of the drugs that are currently in development or have been approved historically, you only cover about 10% of the genome, and that's not even talking about all of the diversity that each one of the genes can put out. Really, 90% of biology has not been explored from a pharmaceutical sense. That's because we have the same techniques, we have the same data sources, and so you're going to end up drilling down the same holes and getting to the same answers. If you want to start bridging out and actually exploring the other areas, you need new ways of doing it.
You need new ways of creating and analyzing data. That's what we've been doing for over a decade. I think you're starting to see other people really understand that and start to adopt that. The good news is, even if everyone switched over to our way of doing business today, you still have 90% of biology that's unexplored and so much to do around chemistry as well. There's plenty of room for all of us to play in.
Related topic, tech space has this attitude that everything is possible today. I can do anything. This assertion is reliant on large linguistic data sets that are offered by the internet and human activity, which we both know is not that long-term.
Right.
We don't think there's a true parallel data set in biology.
Yes.
Do you guys agree with that kind of line of thinking?
Yeah. It's really interesting because, almost building off of that last point, the data's got to be at the core. I mean, the reason you talk to ChatGPT and it can give you decent social advice is because there is a wealth of data on how humans interact socially. You just do not have that on the biological or chemical level. I mean, chemistry is there's 10 to the 60th potential medicinal chemistries. We've barely even hit a drop in that. Biology, I just made the point on. You really need to be creating differentiated data to build the models on. The models themselves, like how you do the models, there is still a lot of capability and differentiation in the teams of people that can do it.
Over time they will commoditize more, and then you're going to be focused more and more on how do I get differentiated data to get to a different answer, and how do I integrate those things? One of the things that makes such a difference to us is we are not a point solution. We see this time after time where, okay, let's take what Recursion was originally known for, the phenotypic discovery platform. It's a great platform. It's a totally different way of looking at biology and allowing the cell's biological process to be as complex as it wants to be and doing an image analysis of it. However, even that dataset is going to have a lot of fuzzy data in it, as every dataset does.
What you need to be able to do is say, okay, I think I'm seeing a signal here. Do I see the same signal here in transcriptomics? Do I see the same signal here in real-world data? Can I create some experimental system that will support it as well? It's at the integration of those different pieces that all of a sudden you get much higher predictive probability. You have to have the data, you have to have different orthogonal data, and then you have to be able to put it together. I think that's really what makes a difference. We've also seen a very stark difference in ability to learn and develop quickly if we're actually doing it on applied work.
Almost all of our spend, it's at least 2/3 of our spend, is dedicated to applied programs in our pipeline or our partners' pipeline. We have a real endpoint that we're designing and building and understanding, and so we can iterate on our models, we can iterate on our data, and get better at it. I think it's really hard to do that in a vacuum because you always think your model is more predictive than it actually is when you put it to the test.
Maybe a last one on this topic. We get asked a lot if drug development's a killer app for artificial intelligence, why aren't more investors interested? Where do you think the disconnect is?
Well, this has been fascinating for me, I mean, watching it evolve for a number of years now. What we ended up having to do was use that balanced business model to be both the vendor and the customer in validating what we're doing because there was so little understanding of how it could impact different parts of drug discovery and how that would translate into development, and such a massive established understanding of this is the right way to do business, that we just had to be able to do it on our own. Part of the reason that we started our own internal pipeline was so that we could manage that process and validate, hey, we are getting differentiated chemistry, or hey, we do have a novel biological insight, and drive that forward and be able to talk about it.
This has been a really interesting process, I think. Someone who is more comfortable in the tech environment is more used to investing in sort of the potential of the market opportunity and trying to think about total addressable market and where do you go with it. I think in the biotech sphere, it's always been data-based, right? Like, what does the data say and what can I draw from that? To be able to transition from what was originally a much more innovation and tech-based shareholder register to really integrating more of a balanced profile along with biotech investors has required us to create data and be able to talk about data.
That's what I mentioned in the first comment. We're really just starting to turn over the cards that biotech investors really care about. I think, look, if those data points go well, it'll be hard not to say that AI played a key role in it because it's everything that we do and the points of differentiation that it would be successful on, those are things that we tried to solve with AI.
Do you think there's potential for an aha moment, like a drug that would go into market or some larger move as it relates to this space? Because one feedback that we do get is there's just no clean proof of concept. I would argue a little bit otherwise. There are marketed drugs, placebo and IDHIFA come to mind that are generated this way. What do you think is that aha moment, if such thing exists?
Yeah. It's interesting. I think that, initially, it'll probably, from a biotech investor viewpoint, I think, until there's a couple that have really shown differentiation based on something that the rest of the industry couldn't do, that people will still just look at it as individual products. To some extent, that's okay. Look, if we have successful products that are going through, the value attributed to that will help us continue driving the value of the business overall. I think at some point, there is an aha. I don't know if it's a wavefront. I think it's more of a distribution that happens over time rather than a single event like we saw with OpenAI. I'd love to see a ChatGPT moment. I just am more skeptical of that happening.
Moving from philosophy to practice. Collaborations, what can you tell us about expected milestone payments from your pharma partners, if you have any visibility this year? Can we anticipate larger payments for foundational work? Kind of like what you've shown with your neural map?
Yeah, really good question. Our partnerships or primary partners are Sanofi and Roche. They both continue to go really well. With the Map milestones coming in from Roche, we're at over $210 million coming in from Roche. I think our real focus there is let's translate the maps, which had never been done before and are really focused on entirely new targets in neuroscience. Let's translate that into programs. That's a core focus for ourselves and Roche, this year, and hopefully we'll have some good events on that. On Sanofi, that partnership is quite different because that came into it with, "Hey, here's a target we want to do. We, Sanofi, as well as the rest of the industry, have never been able to solve problem X." That could be targeting the pocket, or it could be, "You know what?
This is something that's a big multi-billion-dollar industry, but there are known issues with the drugs in that industry. Can you solve it? That's a different type of problem. The fact that we've already advanced five of those candidates through early discovery, what that early discovery milestone means is we think we've solved the problem. Initial testing, the preclinical and non-clinical testing has shown that that problem appears to be solved or the problems. Now we have to make sure it's a good drug that will go into human testing. That's what the difference between the early discovery and the development candidate milestone is. Those development candidate milestones, which are the next ones for all five, are larger milestones. The other really nice thing that I love is they end our operational obligations.
That's all just profit that drops down to the bottom line. Every milestone that we get after that is profit as well. For each Sanofi program, each one can have $343 million in milestones, $193 million of that is pre-commercial. This isn't massively back-end loaded. We've got just beautiful royalties on it that would average in the low double digits. Now, we haven't given explicit guidance for what we expect, as far as upcoming milestones and timing, but I would say both of those partnerships continue to go well, and we're just going to keep on trying to both expand them as far as expanding more programs that are running through them, but also just executing on the pipeline and hitting milestones.
Very closely related question. Sh ould we expect any clinical opt-ins in the near term? Are we reaching that level of fruition? This is something we specifically pay close attention to.
Yeah. From our internal pipeline, you mean?
No.
Oh, from the partners.
From your internal pipeline.
Yeah. We can answer both questions. On the partner programs, when they do development candidate, that is essentially saying they'll still need to do some IND prep work on it, but you wouldn't hit that milestone unless you were intending to take it into the clinic. That'll have a milestone not only at the development candidate, but then also in the phase I, and so we can start to see those come in. It would be really surprising for us to hit a development candidate milestone and then not have them take it into the clinic.
That is actually really helpful.
Yeah.
You touched on this, but maybe just to form it a little better, how should we view cash flows from partners over time?
Yeah, what we would like to do, there's always the question of expansion versus profitability. The way that our partnerships are structured, we want to get paid upfront for our direct costs. We don't want to be investing cash off of our balance sheet for our partner programs. We try and always run our partnerships at a neutral to mild profit until they start to hit the development candidate and beyond for Sanofi, because then they can become very profitable. It's a question of, all right, how many more programs do you want to continue expanding? Because then obviously you're not really reinvesting your profits, but you're delaying the margin associated with it, is what effectively happens. You're also expanding your pool of potential milestones and downstream payments. That's what we're always thinking about.
We don't have a specific hurdle for either Sanofi or Roche. What we really focus more on is this a good program. If we're successful technically in what we do, which we're usually pretty good at, is this going to be something that Sanofi and Roche are going to want to put their entire franchise behind and build up? Because that's when we're not only going to hit those development milestones, but that's where the royalties can kick in downstream and get really profitable.
We do hear reports on pharma and large tech collabs every other week now. How can a small biotech like yourselves compete in shifting attitudes in pharma? You kind of touched on this r ecent reports on Insilico come to mind.
Sure. Well, first of all, I think there is room for everyone's different business models that they want to run in it. I think Insilico is a little bit different in that they probably go after a broader swath of potential partners than we do. We've been really trying to focus on what's the maximum value that we can get out of each individual partner, which actually generally means a little bit more exclusivity around each individual partner.
I have no doubt that Sanofi and Roche pay us more because we don't have 20 other partners that are going along with it. In that sense, I just think of them as different business models. Not one's better and one's worse, but just different. We also do a lot more in-house sort of data generation and sort of proprietary biology, different aspects like that make it a little bit different in how we structure things.
Maybe a last one, just a thinking point as it relates to additional strategies, just given the power of the platform, how do you guys view business development opportunities? You have a relatively strong engine for vetting things.
Yeah. No, absolutely. Well, it's interesting because we now have biology, chemistry, and sort of the clinical and real-world data side of it. We have a lot of different ways that we can contribute value. Obviously FAP was an in-licensed program. The biology platform gave us a novel insight that no one had ever figured out before, and so that was before Recursion had a chemistry arm. We went out and in-licensed a really nice compound for it, and that's worked out really well. There certainly could be other opportunities like that where we just have a differentiated insight on it. We'll keep looking around because there's a lot of different ways that we can bring value.
This is a great segue, so let's talk a little bit about the clinical pipeline, starting with FAP.
Sure.
We're waiting on a regulatory update, but what do you think is the best-case scenario here?
Yeah. The best- case scenario is wherever we end up. I feel like we've got a lot of different ways that we can optimize around it. If you think about it, there's a couple of different levers. One is what are the clinical outcomes? We may be able to move some of the precedent that has been there before because no one's ever done the natural history studies that we did. Nobody has ever showed the depth of response that we did. No one's ever showed it as fast. No one's ever shown that they can maintain that response over time. Those are four new pieces of data coming in for the FDA that we can talk to them about, and potentially that changes what a good clinical outcome could look like. That's one thing to talk about.
Even if it doesn't, what we really want to do is understand which are the right patients to be treating, where is the highest unmet need. We've been able to demonstrate that we can enroll these patients faster. It's very interesting, actually, because a lot of people don't think about it, but the FAP patients are going into their gastros, it can be every couple of months, and they do this for decades. Actually, we have a really good sense of where all the patients are and who their doctors are and how to get to them, right? Being able to use those sort of levers to drive enrollment in clinical trial and trying to decide which of those patient categories we want to target in the clinical trial, I think those are all important questions.
There are also some around where in the treatment stage do you want to engage. It's interesting. It's not a normal situation here because, let's say, a lot of people ask us about pre-colectomy or post-colectomy. Even if they're post-colectomy, those patients go on for decades, where they're still going into their gastro every couple of months because the polyps actually just continue to expand. They go upper and lower then. They'll go into the pouch, they'll go into the duodenum. You have to just continuously be going into the doctor. There's not a segment of the population that doesn't have need. 100% of their polyps are precancerous. I think it's a matter of us trying to decide where we want to target in the clinical trial and how we want to do it.
I mean, some of the past experiences really did revolve around polyp counts. As a key measure. Like you said, there could be ways to look at it in outcomes. Removal of your colon, et cetera.
Yep.
Clearly there's several potential outcomes here. When do you guys think you're going to be able to tell us?
Yeah. Well, we're in process and going through everything. I'll never get in front of the FDA. They've been good partners with us on everything. We haven't seen disruption come out of any of the recent events. You also never know until it's done. We'll let the process continue to go and provide investors with updates as we have them to give.
As it relates to clinical updates. When should we expect additional information?
Yeah, the four other clinical programs that are advancing CDK7, that's got combination data coming first half of next year. Really exciting there. If you think about the CDK4/6 class, I mean, that's almost a $15 billion drug class right now. CDK7 should have broader applicability, but hitting that therapeutic index, it's a tight window. That's what I'd say is a really high risk, high reward outcome. That would be really exciting potential combination data first half of next year. MALT1 is also first half of next year. That's one where we've seen in other compounds a biological effect, so a good monotherapy biological effect, also a combination therapy biological effect. All of the drugs that have been developed to date have a hyperbilirubinemia issue, which is a real problem because clinically that would almost always be used in combination with drugs that cause liver tox.
We think we've designed that out, and you'll actually know pretty early on, right? It won't take that many patients to say, are you having a UGT1A1 issue or not? That data, we're excited about coming up. I skipped over RBM39. That's the nearest one, and I'm sure you will ask a question on it if I don't bring it up anyways. First half of this year, we'll be giving an update on safety and PK. It's probably an earlier update than people are used to, but this sort of underscores what we want to do is treat this like a business model, not like a pet science project.
Kill them fast, basically.
Kill them fast if the data doesn't support it. This is a target that's never been drugged in this way, and it's a degrader. You want to see good safety, good PK. You want to know that you're getting the selectivity and that you can keep going. We're going to take a look and hopefully move it on. If it's not good, we're going to kill it quick. As goes for all of our programs. We would expect the PD data and anything else that we can present coming later on after that. LSD1, which is actually a really cool program as well, that is just kicking off into the clinic.
That's probably late 2027. This is something where we know that LSD1 works but causes thrombocytopenia, and so everyone's basically taken their LSD1 out of cancer and put it into diseases where you get too high of platelet levels. If we can get that therapeutic index level right, then it could be a really nice drug as well.
Well, maybe another moment of philosophy just because I really can't skip it. Is there any concern that once you start generating value from your clinical programs, for example, FAP, you're going to start being valued only on those programs? You'll become a rare disease company instead of a platform company.
What I'd love is just some balance. I think that what's interesting is we actually appeal to a much broader investor base than a traditional biotech. Historically, as I said earlier, we really appealed to a lot of the tech and innovation investors who saw what we can do and said, "You know what? If you're actually able to scale that, then it could change the industry," right? That was a lot of the original background. At that point, it was too early for biotech investors because we didn't have enough data to be able to dig into. I think what we're seeing now is that data start to come through.
My guess would be the first drug that comes through, we're probably going to be valued more on that first drug, certainly by biotech investors. If we start to have multiple drugs that are showing success, then it's going to be hard to draw a different conclusion than the AI is making a difference. If you want inside a single company watershed moment, that could certainly be one of those.
An item that's near and dear to your heart. Control of spend is a recurring theme f or Recursion investors, what's your strategy on this one?
Yeah. Well, hopefully people noticed we took 35% out of the expense base last year, while integrating the companies, while advancing all of these programs. This was a huge priority for us. The way that we do it is actually just by making much better decisions. What we did last year is we actually rebuilt all of our backend systems and put in place a outcomes-based budgeting and business model. What that allows us to do is break down and individually say, for each program, this is what we're spending on it, for every dollar that we have, this is where it's going, and to do that with very high fidelity. What that means is, we can look at it and what we did in our budget projections, for example, is we assumed that all of the clinical programs go forward.
We probability-weighted the partnerships and our own internal discovery efforts. If we decide, no, a program isn't going, we know every dollar and every operation that we need to shut down on the other side of that, right? We can go in and be targeted and be fast. At the same time, we are also just going through every operation that we have. There's not a part of G&A or tech or clinical or anything else that we do that isn't constantly testing itself. Can you do this better, faster, cheaper, right? We obviously know a lot about AI, and so we've been an adopter of agentifying. We've been an adopter of automation. We actually utilize the knowledge and resources that we have across the company to help bring down that spend wherever we can. It's literally just an ongoing, that's how we do business attitude across the company.
Great. As it relates to your cash runway and potential financing. How do you view this? How are you guys looking at things?
The guidance that we gave for 2026 is less than $390 million in cash expenses, and that is not offset by any inflows that we have from our partnership business or otherwise. That's just raw cash expense. I am very focused on bringing that number down, and I know Najat is as well, to below 390 and keeping to modify that. Obviously, part of that'll depend on data and where things go. If we look forward and we assume that all of our clinical programs are going ahead, we can see cash runway to early 2028.
I think we would like that to be longer and we control part of that with our spend, and we'll continue to manage that, as I was talking about. The other part is we're going to continue to figure out how we can best finance the business going ahead. In the end, we're a biopharma and that's part of the industry. No specific guidance beyond that.
All right. We're pretty much on time, so maybe if you have like a last point, something that you feel investors are missing or anything that we didn't discuss that you want to highlight.
Yeah. I think the only point that I'd bring up is a lot of people are out of date on the story is what we find. They view us as a phenotypic discovery company or they know maybe one or two of the older programs. We've just changed so much. It's not just the management team, it's also parts of the pipeline, it's parts of the platform. It's the strategy as well, and it's continued to evolve really. We've learned from the things that we did well and the things that we didn't do well, and we've really come out of it as a different and better company on the other side. I hope people are paying attention.
All right, Ben. Thank you.
Terrific. Thanks, Gil. Bye-bye.