Recursion Pharmaceuticals, Inc.

Hello and welcome everybody to Recursion's Q2 2025 earnings call. My name is Chris Gibson and I'm the co-founder and CEO of Recursion. And I'm excited to share with you today some of the latest updates on our company as we drive forward to Decode Biology. We've been talking for the last nine months since the business combination with Accenture about the Recursion OS 2.0. And I want to start there today and tell you a little bit about the way that we're bringing together the incredible components from both Accenture and Recursion and building new components of the OS in order to drive forward our mission. At Recursion, we base everything off of proprietary fit-for-purpose data, whether it's data we generate in-house or data that we pull from partners. And we're not just generating data to help discover targets or to help translate programs or to help with clinical trials. We're building a true end-to-end capability from target discovery all the way through to clinical trial simulation. We're really, really excited about the way all of these pieces fit together and add to each other. And everything we do at Recursion is based on iterative cycles of learning. Much of our work based on iterative cycles of dry lab predictions and wet lab validations. I want to talk about a few of the pieces of the Recursion OS that we really, really leaned into in the last quarter. And I'm going to start off with talking about BoltSue. This was a really exciting partnership with both MIT and NVIDIA, where we were able to help lead the field of protein folding and lead the field of protein ligand binding predictions with this work that we did with MIT. And we were able to actually open source this project. And today there have been almost 200,000 downloads and almost 50,000 unique users. And what I think is most exciting, what's gotten the most traction about this work is that we were able to actually make binding predictions that are approaching the level of the level of. efficiency and the level of efficacy of free energy perturbation calculations, but we're able to do this with about a thousand-fold less compute. That is really, really, really exciting. It means that a lot of the sort of real bespoke work that was done with physics-based computing could actually be done in a screening format. And while there's more work to do in this space by us and many others, we are very excited about the way this tool and tools like this are going to be able to drive the field forward. And what's more, we've already built this technology into the Recursion OS and even improvements on this technology into the Recursion OS. Another area we've been talking about for the last year has been our ClinTech platform. And this is something that we are now deploying against every single one of our programs at Recursion. There's multiple components to this. The first is our causal AI applied to human genomics. And this is really exciting. We're taking patient data that we get from Helix and Tempest. We're combining that with our perturbation biology data and algorithms from Recursion to help to connect our platform to patients. And this is enabling us to identify targets, to stratify patients, and even to do indication expansion. We've also started to design and simulate our clinical trials at Recursion using in-house software that we've been building. This is allowing us to potentially improve the optimal dose for 30% more patients. This is really, really exciting. And again, we are now deploying this against our programs at Recursion. And third, we're now using our AI, not just to identify patients, not just to design our clinical trials, but actually to recruit and execute. The operation side of our clinical trials is really, really important as well. And with the new software that we've built and the partnerships we built in this space, we now have the potential for 50% faster enrollment projections at high quality sites. And this means we can activate trials up to two months faster. Again, this is the early days of our ClinTech platform, but what I'm most excited about is that we're already deploying these tools against the programs in our pipeline, and we'll be deploying these against new programs in our pipeline soon. And Najat's going to be able to tell you more about that in a few minutes. We continue to advance a pipeline of both internal programs in oncology and rare disease, as well as a suite of R&D collaborations and programs with our partners of Roche, Sanofi, Bayer, and Merck KGA. And we're really, really excited about all of these programs today. But what I think is most exciting isn't any one of the programs. It's the platform we're building and these leading indicators where we're demonstrating that we can bring medicines to the clinic faster and at lower cost. And ultimately, these leading indicators are things that we believe over time are going to continue to improve. And we're going to be able to continue to raise a high bar of quality on our programs and drive them forward at real scale. And to tell you more about the way we're building momentum, let me turn it over to our Chief R&D and Chief Commercial Officer, Najat Khan. Najat?

Thanks, Chris. Great to be here today. So let's dive into this. Chris mentioned, you know, the suite of partnerships and partner discovery programs and internal programs that we are progressing. A couple of things to note on the internal side, you know, you can see there's six or so programs that are going through really, really important inflection points, both across oncology and rare diseases. What I'll do today is double click a bit more on a couple of our more late stage or later stage oncology programs, CDK7, monotherapy dose escalation, as well as the initiation of our expansion cohort slash combination arm and RBM39. You know, we'll share a little bit more around the biomarker enrich, the solid tumors, the patient populations, et cetera, and how we leverage our platform insights in order to hone in on where we go. On the partnership front, you know, I get this question a lot, so I just wanted to step back for a second and share. Across our partnerships, there's two major areas of value creation. The first is really around what Chris mentioned in the beginning, proprietary fit-for-purpose data sets that we're co-developing with our partners. So an example of this is, of course, the phenomap, the first neuronal phenomap, iPSC-derived with Roche Genentech. And the other area of value creation is around partner programs where we are designing using our AI modules on the chemistry side, very challenging first-in-class, best-in-class programs. And just recently, we achieved a fourth milestone in our Sanofi partnership. More to come on that. So just going to the next slide, I'm just going to take a second to do a quick snapshot on the overall programs that we have in our internal portfolio. And then I'll go a bit more into CDK7 and RBM39. So just as a quick reminder, CDK7 really important target. The focus really is leveraging our AI powered design module in our recursion OS platform to optimize the therapeutic index. This is a target that has been tried. by others before. So that's the area of focus. We should have more monotherapy dose escalation data by the end of this year. And as I mentioned, combination initiated. RBM39, this is an example actually identified using our phenomabs where we identified a new MOA with synthetic lethal targeting opportunities in genomically unstable cancers. More on that. First half of 2026, we anticipate some initial data from a monotherapy dose escalation. You heard a little bit about the MEK1-2 in our FAP program. So I just want to highlight, this is again another phenotypic insight where we actually derived the fact that there's a connection, an important relationship in an unbiased fashion between MEK1-2 and the relationship with MAP kinase pathway, signaling pathway, and APC and Wnt signaling pathway. Which disease? This is for FAP. So again, we should expect more data beyond the initial cut we shared earlier this year, second half of 2025, end of this year. And MALT-1, this is another program where now we're using and leveraging our AI-powered chemistry design portion of the RecursionOS platform, again, to lower the liability that's associated with U2T1A1 inhibition that's also in monotherapy dose escalation. And to round it out, we also have a couple of preclinical programs here that are going through important inflection points in the development candidate slash IND enabling phase. But, you know, a lot of these programs, and we talked about this before, were really focused on the earlier versions of the recursion OS platform. And as we iterate and learn and add more components to our recursion OS platforms, we expect the next wave of programs to be even more high potential and potential to do it in a more efficient way. But I wanted to take you a little bit under the hood of what's actually in the Recursion OS, especially the 2.0 platform following the integration with Excientia. So if you just look to the left-hand side, we first start with the AI-powered biological insights. This is where we are actually deriving novel targets. This is from multi-omic data, whether it be phenomic, transcriptomics, et cetera, connecting that early on with the patient. This is the ML-based patient connectivity data layer that's really important to data sets such as from Tempus, Helix, and others, ensuring that we can actually take these biological insights and deconvulate the MOA, and very early on do a screening approach around triaging what are some of the binding affinities early on. So this is where approaches such as BOLTS2 that Chris mentioned earlier is already being incorporated into our workflow. In addition to that, we're also developing proprietary algorithms in-house. So as soon as we put this on a slide, I have to say it gets outdated because there's so much rapid iteration and work that's happening. In the middle, AI-enabled precision design, this is where we're designing our molecules, really optimizing both for novel scaffolds, this is where we use generative AI approaches, and also active learning in order to optimize drug-like properties. This also includes using QMMD approaches, which is a 3D protein and atomistic models. And one important point here is the wet and dry lab integration that we have. So this is where aspects around automated chemistry, automated biology, and automated ADMET becomes incredibly important. So we can design out certain elements earlier, faster to ensure that we have better molecules out of discovery. And last, but certainly not the least, and one that's close to my heart, is ensuring that we do this also in clinical development. Chris touched on this in terms of some of the areas that we're building out, and you'll see some of the examples we're using in our current programs already around causal inference on patient stratification and also smarter trials and faster equipment. So as I go through each of the programs, I will actually highlight which area of the Recursion OS module and platform we are integrating and actually in uh highlighted in insights for our program so let's start with rbm um 39 so in this program as i mentioned earlier the focus was really around leveraging our maps of biology. So just as a reminder for everyone, starting on the left-hand side, we start with these really large maps of biology, whole genome CRISPR knockouts. And then we profile compounds that are proprietary to us in order to get better understanding of the initial chemical substrates that might actually modulate the biological insight that we have identified. So the example here is how we identified RBM39, which phenomimics CDK12. So CDK12, and this is to the panel to your right-hand side, has been an attractive target in oncology for its role in DDR modulation, but generally has suffered from challenges in selectivity because of how homologous CDK13 is. Leveraging our phenomaps, we actually identified that RBM39 is similar phenotypically at least to cdk12 well we could and not to cdk13 so that was the first insight the second insight was the fact that we were actually developed we're able to develop molecular glues and degraders for rbm39 which you'll see in a moment that are also phenotypically mimics cdk12 So this was our first inkling that this could potentially, RBM39 inhibitors or degraders could potentially provide a druggable potential analog. And then I want to say something else that doesn't get talked about enough, which is if you look at the middle panel, we also look not just for CDK12 or CDK13, but we look more expansively across the map to see is there well-established dependencies that are known of already biologically that are also being validated. An example here is acetic acid 12 and cyclin E, cyclin K, similar phenomic readout. But this is just a small detail in the entirety of the map that we look at. And if you go to the next slide, This is another expansion of that same map. And what we see here that's actually quite intriguing is in the center in the black box is what I was referring to in the earlier slide, which is the RBM 39 and the degrader itself and some of the associations that we see with CDK 12, CDK 13 and so forth. But you look broader and you also see associations mechanistically in DNA damage repair, epigenetic regulation, cell cycle control, and transcription. And this, when you look at it from an MOA perspective, which I'll turn on next, actually intuitively makes sense. RBM39, if you go to the next slide, is focused, is important for splicing fidelity. degradation of RBM 39 leads to splicing defects. Now, if you combine that with tumors that are already already genomically unstable, whether it's because of DNA repair pathway vulnerabilities or transcriptional regulation, then that can actually increase the amount of instability leading to potential apoptosis and cell death. So just want to share with you how an insight is then triangulated with understanding of mechanism of action. but that's not enough. So if we go to the next slide, in addition to that, we also looked at in vitro and in vivo work. Starting with, look, when we look at the broader patient population, just given the connectivity across the maps that I noted, for replication stress, tumors that suffer from epigenetic dysregulation, cell cycle alterations, or oncogenic drivers are relevant, as well as those tumors that have DDR effects, so both of those. And that spans several solid tumors from colorectal, breasts, et cetera. along with some pretty clinically actionable alterations that we'll be studying and looking into more such as MSI high, make amplification, et cetera. But we wanted to look at the in silico understanding and triangulate that with in vitro and in vivo work. So if you look at the in vitro cell lines, you clearly see that RBM39 degrader, so REC1245 in this case, there is greater sensitivity in cell lines that have higher replication stress versus cell lines that don't have higher replication stress. So this was a good early signal for us. And if you go to the next slide, we see a similar trend hold in in vivo as well, where you see a reduction in tumor volume across different tumor types that actually have high replication stress signatures. So this helps us to do things. Number one, better understand the importance of RBM39 as a first-in-class target in solid tumors. Second, also give us a better sense in terms of which patient population, tumor segments, et cetera, might be relevant for us to target. And if you go to the next slide, we went a step further than that. We also wanted to look at the totality of it. So you have the recursion OS inside, definitely the preclinical data that I mentioned, but also looking into mechanistic validation. in the middle panel. And, you know, we see two things here. First, the DMACC is approaching almost 100% in RBM39 degradation with quite potent D50 numbers as well. So rapid and potent RBM39 degradation. Now we wanted to go even a step further if you go to the next slide, which is, if you go on slide before, please. OK, that's OK. If we go to the next slide. So this has actually helped us inform what our dose escalation and our combination arm is going to be. So for RBM39, monotherapy dose escalation, but in terms of the cancers that we're looking after or going after, it's endometrial, ovarian, et cetera, with cancers with high genomic instability. And we will also be focusing on some of these biomarker-enriched populations, such as MSI-high. So again, first patient dose, patients are enrolling in this study. We should have early safety and PK data from this monotherapy trial in the first half of 2026. Now, we'll go to CDK7, which is our next program. Here, we actually leveraged two components of our RecursionOS platform. First, focused on designing a molecule that can really optimize for the therapeutic index. Second, leveraging some of our ClinTech approaches in order to hone in on which patient population and which combination arm we will hone in on. So let's go to the next slide. OK. So just a quick reminder in terms of how the molecule was designed, a couple of things to note here. CDK7 has been an important target for some time as well. It is a master regulator, both cell cycle progression as well as transcription. But one of the challenges that other compounds have seen so far is challenges with permeability, efflux, and not rapid absorption. So we want to change that around. We use generative AI models to actually design new scaffolds. And I think this part is really important, which is leveraging active learning and experimental ADMET data to quickly learn, iterate and optimize the molecules to reduce the components that we wanted to design out, such as ensure that there's high permeability, rapid absorption and low efflux. And similar to RBM39 degrader, which was done in a very short amount of time, 18 months from start to IND enabling, with about 200 compounds or so synthesized, in this case, you also see about 136 novel compounds synthesized and getting to candidate ID in less than 12 months. Now, one of the components for designing high permeability, rapid absorption, and low efflux was to ensure that we would have sufficient exposures. And you see that on the right-hand side panel, both 10 milligram QD, 20 milligram QD, clearing the IC80 line. And when we actually look at versus some of the peers, it's an order of magnitude higher than the exposure that they're seeing. So as of November slash December 2024 data cutoff, the compound showed one confirmed PR in ovarian cancer, as well as multiple cases of stable disease. So far with a favorable safety profile and no MTD reached. If you go to the next slide, what we have done since then is really design which combination arm we will focus on. So the one that we're going to focus on that we've announced today is second line plus platinum resistant ovarian cancer. How do we get to that? So first we looked at preclinical data. So cell panels, in vivo, you see an ovarian. Both of them are sensitive to CDK17 and there are multiple panels that were done. And then in addition to that, as part of our ClinTech approach, we also use causal inference using some of this multi-omic and clinical data. And this was very important to better understand the cause and effect factors here. And what we see is that a higher expression of ovarian cancer based on this data is associated with lower overall or worse overall survival. This was based on about 32,000 patient records. So this gave the totality of the evidence from preclinical and also some of what we see in our early clinical data so far, combined with some of this causal inference work, gave us more confidence in terms of the first indication that we would go after. Were there significant unmet need in second line plus platinum resistant ovarian cancer? So if you go to the next slide, site selection and activation is in progress right now. For the combination arm, the standard of care includes single agent chemotherapy, BEVA plus chemotherapy, and in some cases PARP inhibitors. In addition to that, the monotherapy arm is ongoing, and we anticipate more data from that later on this year. Go to the next slide. So I'll also share a bit more about some of our partner discovery programs. Next slide, please. Great. So if you look at Sanofi as an example, just mentioned that we have our fourth program milestone achieved in the last 18 months. I just want to take a moment to say that some of these programs, both in immunology and oncology, first in class, best in class, some of the milestones that we're going through include important milestones in discovery, lead series, development candidate, and so forth. And we have several programs advancing to those milestones, including development candidate in the next 12 to 15 months. This effort leverages what you saw in the Recursion OS platform, a lot of the AI-powered chemistry design module. And in terms of Roche, you know, five phenomaps built to date. So you saw an example for RBM39, how we use some of our phenomaps. These are specific for the neuroscience and GI-ONC space. I mean, for the neuroscience one that we delivered last year, over a trillion iPSC-derived cells used, whole genome knockout, and also other perturbations in terms of overexpression. So you're really getting a very holistic understanding of biological pathways. and a lot of work in progress there in order to take those insights and translate them into novel programs. So more to come on that. And then also on the GI on indication over four maps already developed there and already one program that has been optioned and more work happening. And I think one point to note here, it's a real pleasure and honor to partner with partners such as Roche, Sanofi, Bayer, and Merck KGA, where we bring the best of our capabilities, the Recursion OS, the Recursion Drunk Hunter expertise, and the platform tech expertise, along with the deep biology expertise and chemistry expertise in Genentech, Sanofi, and others. And then when it comes to Bayer and Mercreate GA, similarly, you know, the second area of value creation that I mentioned earlier, which is challenging targets, developing molecules for them using our chemistry platform, or actually highlighting and nominating novel or undruggable targets from our maps of biology. With the potential here, a lot of work ongoing for over 100 million in partnership milestones by the end of 2026. So with that, I'm going to hand it over to Ben Taylor, our CFO and President of UK, to tell us a little bit more about our financial update. Ben.

Terrific. Thanks, Nija. So we had a good quarter and ended with a strong cash balance as we go to the next slide, showing $533 million in cash at the end of the quarter. That was based on not only managing our expenses. So at the time of the merger, we made a commitment to our shareholders that we would not only drive a lot of the growth and the programs and the technology that Chris and Najat talked about, but also manage our expenses. And so you've seen us go from a pro forma burn in 2024 to a expected cash burn in 2026 that's 35% less. And that's really our commitment as a management team to making sure that we're doing this as efficiently as possible. We had some great cash inflows over the quarter. In addition to the Sanofi milestone payment, we also had a 29 million R&D tax credit. This is a UK tax credit. We will continue to receive this in the future, although it will be smaller as the legislation around it has changed. Our guidance has not changed and we continue to project over 100 million in partnership inflows by the end of 2026 and managing our burn below 390 million in 2026, so next year. All of that comes together with an expected cash runway through the fourth quarter of 2027. That cash burn number that I gave you does not include any partner inflows or other financing or inflows that would come in. And with that, I will turn it back over to Chris.