Recursion Pharmaceuticals, Inc.

Hi, everybody. My name is Chris Gibson, co-founder and CEO of Recursion. And I'm delighted to welcome you to our learnings call this morning. We're going to go ahead and get started. Perfect. So of course, important to note that we're going to be providing forward-looking information today. So please understand all of these important caveats. So I want to begin by just talking a little bit about Recursion's mission, which is to decode biology to radically improve lives. And unlike a traditional biotech, where learnings from a program typically work within that specific program, those learnings could improve a program, or the scientists from a given program might take some of those learnings onto their next program, at Recursion, we're trying to do something different. We're trying to build a learning system, a Recursion operating system that learns from every program to make the next generation of programs better and better. And that requires some scale. And what you're going to see today is that we're taking decisive action to make sure that we can continue to both take our internal pipeline forward, our partnerships forward, and also that we can continue to run this critical experiment for the biopharma industry. And that is to build the first great tech biocompany. I want to talk a little bit about our earliest version of the platform, Recursion 0.1. This was a platform built on top of Phenomics and siRNA and repurposing. And today, you'll hear a bit about how some of those programs have done well, like our FAP program with preliminary efficacy data and safety data we'll share soon, and also how some of those programs have not turned out the way we hoped, such as our CCM program. But building on the learnings of that first generation, we built an improved Recursion operating system, Recursion 1.0 that added transcriptomics that allowed us to go after new chemical entities and use advanced tools like CRISPR. And that's allowed us to take forward incredible programs like our RBM39 program and our CDIF program. Some of these programs advancing and others were holding back for strategic reasons today. And ultimately, Recursion 2.0 is what we are now. Post the combination with Accentia, we've seen the power of combining our two platforms, the multimodal data, the compute and the active design. These are allowing us to generate a new generation of early stage discovery programs that we think are extraordinarily exciting. And all of this work is enabling us to demonstrate these leading indicators of success. We're able to validate our hypotheses more quickly. We're able to generate candidates with fewer molecules synthesized. We can spend less and go faster. And through each generation of Recursion's operating system, we expect to improve on these kinds of parameters. And today, we're sharpening our focus, sharpening our R&D portfolio, because we committed to doing that with the combination of Recursion and Accentia, two of the leading tech biocompanies, because we've seen the power of our Recursion OS 2.0 platform. And we want to make sure we can double down on the winners and also make sure that we're the kind of company that can decisively move away from programs that don't meet our mark. And finally, we understand the challenging macroeconomic environment, and we want to be absolutely sure that in this kind of uncertain environment, we're making disciplined and thoughtful decisions to ensure that we can deliver on our long-term mission to decode biology, to radically improve lives. So today, we're unveiling our Go Forward Pipeline. More than five clinical and preclinical programs that we believe have a much higher probability of success. We believe these are programs that are worth taking the shot and we're doubling down on them. And we're going to hear a lot more about each of these a little bit later today. But before we do, I want to also share that it's not just about our internal pipeline. It's also about our partnerships. We brought in more than $450 million earned through these four collaborations to date. Today, we're also sharing that we've received our fourth program option from Sanofi, part of that collaboration. And I believe that through our continued work on programs like those that we're advancing with Rochonentech and with Sanofi, Recursion is not only going to continue building its Recursion Operating System, it's going to continue learning to improve not only our internal pipeline, but all of the partnership programs that we advance together in the future. So with that, I want to turn it over to our Chief R&D Officer and Chief Commercial Officer, Najat Khan. And I just want to say a huge thanks to her and the team that's done incredible work to help us make these important decisions for the future of the company. And with that, over to you, Najat.

Thank you, Chris. Good morning, good afternoon, good evening, everyone. Thank you so much for joining our Q1 2025 earnings call. As Chris mentioned, over the next 40 minutes or so, I'll walk through some of the key pipeline updates, delivering on our commitment to sharpen our focus, following the combination of Exciensia. I'll also highlight the programs we're advancing with the potential for greatest impact and also programs that we have thoughtfully chosen to discontinue. And in addition to that, I'll round it out. So we go back to the next prior slide. You know, Chris shared this slide. I just want to double down on a few more points. So first of all, you know, three key points to consider. One is our pipeline really reflects the strategic application of recursion, OS, and AI, where it matters the most. So as I go through each of these programs, I'll talk about places where we have novel biological insight or areas where we are engineering and designing differentiated molecules, as well as areas that we're driving precision-based development, a really key theme that we're doubling down on further. But every single asset you see on this page is done with one end in mind, which is programs that aim to create differentiated medicines that patients are waiting for. The second point, in terms of sharpening our focus, we're doubling down in these programs, both in oncology and complementing it with a focused effort in rare diseases. As Chris mentioned, we're advancing over five internally developed programs with first or -in-class potential, each targeting unmet needs with a clear and efficient path to development and potential launch. And then the third point, look, as part of having portfolio, but since we did the integration, the portfolio has grown, and we said that we would actually make disciplined decisions to sharpen our focus. This is based on both data, and I'll talk through that, but also strategic considerations. And it includes deprioritizing three programs, NF2, CCM, and C-Diff. We're also placing LSD on a strategic pause as we assess opportunities for a more differentiated TPP. And we've also made some choices in our preclinical programs, a balanced approach both preclinically, in research, and also in development. You will see through the presentations that this move and these moves reflect our clear commitment to a high bar on differentiated medicine following the special combination, while also contributing to capital efficiency by reallocating these precious resources towards the highest potential opportunities. So let's start with some of the go-forward programs from the prior slide. I'm gonna go through further details, but I just wanna hit a few key points as a summary. So CDK7, starting with our selective and reversible CDK7 inhibitor, Rec 617 was precision designed using our recursion 2.0 platform with a remarkable 136 novel compounds of the size, compared to thousands that are typically made. It was designed to optimize that therapeutic index with the goal to improve safety and efficacy compared to our competitor molecules. Early clinical data, which we shared in December from our monotherapy, shows there's encouraging monotherapy activity, including a confirmed partial response in a platinum resistant ovarian cancer patient. And so far, a manageable safety profile. As we committed to before, we will open combination studies in first half of 2025, and further details about that will be announced upon steady initiation. Now, turning to our potential first in class RBM molecular food degrader, Rec 124.5. This emerged from our phenotypic insight that revealed RBM39 as a potential novel mechanism that is functionally linked from our phenotypic platform to CDK12, a historically challenging oncology target. By degrading RBM39, Rec 124.5 is designed to potentially mimic the downstream effects of CDK12 inhibition, disrupting RNA splicing to down-regulate cell cycle checkpoints, DDR networks, et cetera. This triggers cell stress and apoptosis. We advanced this program from target ID to IND enabling in less than 18 months, showcasing the speed and precision of our learning, discovery and recursion 2.0 platform. The clinical development program, as we noted earlier, is focused on a biomarker defined set of solid tumors and select lymphomas, with preclinical studies supporting its PKPD relationship and antitumor activity. The study is now in monotherapy dose escalation. Turning to the next one, Malt1. This is our selective best in class Malt1 inhibitor, which recently entered phase one dose escalation our last quarter with the first patient now dosed. Rec3565 is being developed for relapse refractory B-cell malignancies. Again, another example of a molecule that was designed by a recursion 2.0 platform, especially the generative AI piece, which integrated hotspot analysis and molecular dynamics to enable best in class profile with improved potency, selectivity and safety. And one piece I wanna point out is that the molecule was designed to avoid meaningful inhibition of UGT1N1, a known off-target liability seen in this class of molecules that can drive hyperglycerine. Next, PI3K. This is our most advanced, one of our most advanced preclinical programs. It's a PI3K alpha inhibitor designed to highly selectively target the H1047R mutation, which is a driver alteration presented in about 14% of breast cancers and 4% of all cancers. While PI3K of course is a crowded landscape, this molecule was again developed using our generative AI platform to optimize selectivity from the mutant, as well as over wild type. So about 100x more selectivity over wild type and 10x greater selectivity from some of the wild type sparing inhibitors that you've seen recently. And to date in our preclinical models has not shown any sign of hyperglycemia or GI talks. Early preclinical data is something I'll share in greater detail today, that are showing two regressions at low doses, supporting a potential therapeutic window, a broader therapeutic window that limits liabilities such as hyperglycemia, with the goal to also improve the potential for efficacy. Next, I'll move to some of our targeted rare disease programs, FAP. So FAP, this is an allosteric MEK1-2 inhibitor in development for the orphan disease FAP. Just as a reminder, there are no approved therapies and the unmet need is very high. This is a potential first in disease program that originated as Chris mentioned, from the earliest iterations of the recursion OS platform, showing an unexpected and novel insight between MEK and APC and FAP disease modulation. 4881 is currently an ongoing phase two open label signal CT study. As you know, some of the initial data, including safety, tolerability, and preliminary efficacy of Reg 4881-4 milligram was presented yesterday by our investigator, Dr. Joel Samadar, in a late breaking podium session at the DDW conference in San Diego. I'll walk through some of the similar data to share with you in terms of the pipeline update, as well as the next steps for the program. EMPP1, moving to our second program in rare diseases, -1-2 is a orally bioavailable small molecule EMPP1 inhibitor being developed jointly with RallyBio for hypophosphatacea, which is a rare metabolic bone disease. I'll leave some of the details, but I'll share more in terms of how we have leveraged our platform in designing a highly selective EMPP1 inhibitor, potential first and best of class, and also some of the preclinical data that we have seen recently for this compound. And LSD1, lastly, as noted earlier, was strategically pausing development, a potential best in class CNS penetrant reversible LSD1 inhibitor to ensure some of the internal and external data that would be important to have a competitive TDP. We may pick this program up later, depending on how some of this data evolves. Now, moving to some of the programs that we are de-prioritizing. So let's start with NF2. After a thorough review of the clinical data by the NF2 team, we believe that the decision to discontinue further development is clear. Although phase two for NF2-related meningiomas technically passed the fertility threshold, and this is what we were waiting for, it was primarily driven by the lower 40-milligram cohort, the 60-milligram and the combined difference did not pass the fertility criteria. And the point I want to emphasize the most is we observed limited tumor shrinkage and clinical activity across both 40 and 60-milligram arms. The next program, CCM. For CCM, we initially reported top line data from the phase two sycamore trial, and we were pleased to see that it was safe and well tolerated. While the early data did show some promising trends, potentially, in exploratory efficacy endpoints at 400-milligram, this was both for lesion volume and the Rankin score, there were negative trends in the efficacy of 200. But these signals and data were not statistically significant. One of the things we noted in terms of next steps was looking into the LTE as well as additional regulatory engagement. So what are the new findings? From the long-term extension studies, we do not see promising trends in MRI at decline or functional outcomes. And I want to emphasize a couple of things. One, that we were paying attention to is placebo to 400-milligram crossover, where each patient served as their own baseline. We did not see any trends there across any of the endpoints. And for 400-milligram to 400-milligram arm, we did not see the continuation of prior trends. And further, it was not distinguishable from natural history. So unfortunately, based on the totality of the data, and this was important to actually have an LTE for a first in disease program to give us confidence, we looked at the totality of the data and it's of course the discontinuation of the CCF. And now turning to C. diff. From a platform perspective, we saw a novel insight. This is a new mechanism of action in terms of how we can tackle recurrent C. diff, a highly potent and orally viable C. diff toxin B selective inhibitor. However, as with any of these programs, we're constantly tracking the external landscape. And the recurrent rate, aka the unmet need of C. diff, with some of these programs that are further along, is now reducing to almost around 5%. And without a clear differentiation, this comes to the point we made earlier, without a clear differentiation profile, we've decided not to pursue further development, internal development, and take those precious dollars to double down on areas where we have scientific, commercial, and technical promise. So I just want to round by saying per our clinical trial transparency policy, we intend to make all clinical data publicly available in a peer review journal following appropriate review. And a huge, huge thank you to all of the investigators and patients for supporting us, for being part of the studies. Research and development is one of the hardest things that we do to make new medicines for patients. And it's with deep gratitude. Thank you for being part of our journey. So I just want to take a moment. We talked about go forward programs. We talked about programs where we are making data-driven and strategic decisions. What is that go forward portfolio strategy as a learning organization? It has to be grounded in scientific rigor and disciplined capital allocation. This reflects a raised bar for what we choose to pursue. Programs that meet the highest standards of differentiation, address significant unmet need, and leverage the unique strength and the full power of the Recursion 2.0 platform. So as you can see, we are evolving to a more focused, product-oriented strategy, leveraging the full power of the Recursion 2.0 platform. And that tech stack is not just biology, not just chemistry, but also in clinical. And I speak to that more as we go through some of the programs. Number two, we're focusing and we'll continue to focus on medicines for patients that are differentiated at the time of launch. Those can be first in class, those can be best in class. And third, the how. We are applying discipline execution through rapid data-driven and resource efficient -no-go decisions. In-house AI-driven design, really important. And clear differentiated target product profiles to accelerate our proof of concept and maximize our need resources and time efficiency. So with that discipline framework, what comes next? Let's go to the catalyst slides. We anticipate meaningful readouts and catalysts across our internal pipeline in 2025 and 2026. So the first half, as I mentioned earlier, the initiation of our combination study and advanced tumors, building on the monotherapy insights from December of last year for CDK7. Second half of this year, we will have additional phase one data from the monotherapy as that program matures in the second half for CDK7. In addition, for FAP, we also have additional patients enrolling in our four milligram cohort. So we'll have additional data as well. And as I mentioned with PI3K, we expect Development Candidate nomination the second half of this year. So we can start the all important IND enabling studies for this important program. Transitioning to next year, so first half of 2026, which is the early safety and PK data readout from our ongoing monotherapy trial in biomarker enriched, our VM39 program for solid tumors. And in the second half, similarly, early safety and PK update for monotherapy for our mode one study in B-cell malignancies, as well as phase one initiation for our EMPP1 inhibitor and hypocostal tissue. So that was the overall view of the portfolio. What I'd like to do now is go through some deep dive steps. For FAP, for PI3K and EMPP1, I'll also ensure that you have a one pager, latest and greatest update on all of the other programs, clinical and preclinical, and then I'll round it out with some of our latest updates on partnerships. All right, FAP, next slide please. Lots of words, I'm not gonna go through all the words on the slides, but I think the most important elements are, this is an allosteric MEK1-2 inhibitor developed for FAP. FAP is a rare inherited condition with no FDA approved therapies. Next slide. We leveraged, how did we leverage the recursionist platform? We analyzed cellular models of APC gene loss, which is the root cause of FAP, and we identified MEK1-2 inhibition as a novel therapeutic intervention of this mutation. This insight drove the discovery of REC481, which is a molecule that we in licensed. And next step was to see, did this insight, which was unknown before, play out preclinical? So in preclinical models, REC481 demonstrated significant reductions in both polyp count and high grade adenomas, outperforming celicoxate, which is one of the off-label drugs that are used today, and not approved today. In addition to that, we then decided to go into clinical studies. So if we go to the next slide, please. The trial is a two-part study, evaluating REC481 in patients with FAP. First, starting with a phase 1b safety run-in that you're seeing here, which is four milligram in placebo, and then for the next click, advancing into an open-label phase 2 signal seeking study. To reduce class-related side effects, which are seen with MEK1-2 inhibitors, the phase 2 portion was refined to enroll patients 55 and over. The ongoing phase 2 portion is evaluating two once-daily oral doses of REC481. For the four milligram, the primary endpoints included safety, tolerability, and preliminary efficacy. And the main efficacy point is really percent change in polyburden after 12 weeks of treatment. A follow-up endoscopy at week 25, following a 12-week off-treatment period, is used to evaluate the durability of the response. And as of March 17, 2025, data cut off six patients were efficacy-evaluable in the four milligram arm. And this is gonna be the focus of our clinical efficacy data. But before we go into efficacy, I'd like to cover the safety element. So when you look at the phase 1b and phase 2, the data you're seeing here is among the 19 safety-evaluable patients. We want to show the entire cohort that received 481 across both phases. The most frequent treatment-related AEs were grade 1 and 2, with grade 3 being about 16%, and we did not have any grade 4 or above. Treatment-related AEs reported to date were mostly rash, diarrhea, and there was some left ventricular ejection fraction. When we looked at the phase 2 portion, the most commonly related treatment-related AEs were still rash, CPK, and diarrhea. And I will say that rash and the decreased left ventricular ejection fraction are both consistent with the reported safety profile of approved MEK1-2 inhibitors and our class effect. The LVEF did not lead to any discontinuations. Now let's look at the efficacy. Again, I want to preface by saying these are preliminary results and of six efficacy-evaluable patients. The distribution of polyburden changes across all efficacy-evaluable patients is shown in the waterfall. The time of data cutoff, phase 2 data shows that the 4-milligram led to a preliminary 43% median reduction in polyburden in close collective new patients that are 55 and over. Five of six patients, 85%, experienced reduction ranging from 31 to 82%. However, one patient did show a substantial increase in polyburden number from baseline, which has been shown in prior studies as well, something we need to, of course, investigate and understand further. Three patients, three of the six patients achieved a greater than 50% polyburden reduction at week 13 of the two patients to maintain a durable greater than 30% reduction, even after the off-period, the total off-period in the drug. If you go to the next slide, we also wanted to look a little bit deeper into the efficacy data. Recall that the patients with FEP often develop polyps throughout the GI tract, so both the upper and lower regions. In the waterfall, you see reductions both in the upper and lower regions, with median reductions 50% or higher. These were encouraging and suggest that there's clinical activity across both anatomical sites. We also looked into an important set of endpoints. So I talked about polyburden reduction, but also polyp count and Spiegelman stage, which is an important classifier of disease severity and the potential for cancer rates down the road. So the table shown here summarizes Spiegelman stage in the screening and again at week 13, highlighted in green, you can see about three out of the six patients that are on the formula of REMARMA experienced a reduction in Spiegelman stage, with two patients showing a full change of about a scale of two. While this is early data and of course, anytime you do Spiegelman, it's potentially confounded by biopsy sampling, it is worth noting that prior pivotal FEP trials have included changes in Spiegelman stage as part of the composite endpoint to track FAP related disease progression. So in terms of summary and next steps, we've taken together and I will point your attention to the left hand side first, the initial phase two preliminary data does show a consistency of insight from the platform, bolstered by what we see in vivo and now encouraging early clinical signals. As we look ahead, enrollment for the space to study is ongoing and expect to share additional data with efficacy and safety in the second half of 2025. And now move on to our PI3K H1047 mutant selective program. So RET7735 is a highly selective PI3K1047 a mutant selective inhibitor. 30 to 40% of HR positive breast cancers have PI3K mutations with the H1047R mutation in the kinase domain being the most common, about 14% of HR plus breast cancer. So about anywhere from nine to 11,000 patients in the US and EU. This molecule was engineered as I mentioned before with 100X selectivity over wild type, a demonstrate strong CNS penetration and from the data we've seen so far, a low risk of metabolic AEs like hyperglycemia. I'll walk you through some of the data that's showing superior efficacy to picrain and also CAPI, an AKT inhibitor with synergy at low doses when combined with CdK461 inhibitors and CERD. The study is currently in Canada profiling with a nomination to DC as I mentioned before, expect a second half of this year. So let's just take a second in terms of how does a platform, what is the platform insight here? We started by applying molecular dynamics to characterize the flexibility of the mutant PI3K alpha protein. It allowed us to capture key conformational snapshots that reveal cryptic mutant specific binding pockets. These insights formed our SAR strategy early on, which is really important, giving us a more precise roadmap for design. From there, we use AI ML models to prioritize chemical space predicted to optimize potency and selectivity while also accounting for critical drug like features such as physiochemical properties and AgMet. At any point, I'd love to go through more detail on this.

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tightly integrated AI driven approach enabled us to progress a target concept to differentiate a candidate in 18 months. Again, another example of this learning platform and how we're trying to get better molecules designed faster. Now, if you look to the next slide, which is focused on some of the preclinical data that I just mentioned, Chris mentioned this slide and some of this data in the prior earnings call. So REC7735 shows a dose dependent tumor regression in the CDX models, the PI3K1047R CDX models compared to a better than standard of care PI3K inhibitors. We have PIKRE here, which is one of the first generation wild type, we have Scorpion and also the Lansil compound. This supports a differentiated safety profile while maintaining efficacy. We do not see an increase in hyperglycemia markers, as you can see to the right side. Now, if you go to the next slide, just wanna share some of the newer data head to head with CAPI. Here you see REC7735 at 18.7 megs per case, which is the medium dose, significantly outperformed high dose CAPI into regression. Even the low dose 6.25 megs per kilogram dose was comparable to CAPI. And in terms of tolerability, we did not have any weight loss observed, which we have seen with some of the other agents today in these mass models. We also looked at some additional data in combination with endocrine therapy. REC7735 significantly enhances the effect of filestrin, so CK46, as well as certain inhibitors in the CDS models. At low doses, REC7735 alone was more effective than this standard care combo. And when it's added, we see some further deepening of response. So again, preclinical early data, but a compelling rationale for REC7735 in combination regimens for HR plus or to negative breast cancer. So next steps, as I mentioned, again, we see that arc, right? The biological insight, but I would say much more focused on the biological insight in terms of greater selectivity by going after the most common mutation in PI2K, the design elements that I just mentioned, novel scaffold, and then also some of the in vivo data that we're seeing, which is encouraging for us as a potential best in class therapy. DC nomination is planned for second half 2025, with again, the goal of addressing a clear patient need in a genetically defined population. Next, I will share a little bit more data about ENPP1 inhibitor shifting to rare disease. REV102 is a potent highly selective ENPP1 inhibitor redeveloping with our partner Rally Bio for HPP, a rare debilitating bone disorder with limited treatment options, particularly for adults. The treatment options today are injectables, three to six injectables a week, injections a week, and that is challenging. And so we think that there is a significant patient population that would benefit from a first world non-immunogenic disease modifying therapy that can reduce the burden and cost of lifelong enzyme replacement therapies, which is the current standard. So again, platform insight here, this is another example of the recursional as delivering targeted innovation. We identified structural insights into ENPP1 and used our generative AI design and ML driven optimization to create novel scaffolds with high safety margins and oral dosing potential. This was not easy, this is not for the faint of heart. This took a lot of reps, but by modeling human agony early, we built confidence in the clinical profile before nominating a candidate. This is how we wanna front load some of the risks in our now go forward ad portfolio purchasing strategy. So looking at some of the preclinical data, so I'll show you two slides on this. The first one is Rev1 and 2 in early onset HPP models. In an unpublished early onset knockout model, Rev1 and 2 significantly extended survival, so you can see on the left hand side, survival lines for the early onset HPP models. And when you look to the right, you will see also reduced PPI levels, which is a known biomarker that is critical for bone mineralization and restore bone density close to wild type levels. You will see on the chart to the very right, the ALPL knockout group was not shown, given all of the mice died around the human age. These data suggest the compound can address both biochemical and skeletal aspects of HPP in early onset models. But we also wanted to look at late onset models, right, which more closely mimics adult HPP. And as you can see to the left hand sided, corrective key skeletal defects and normalized the patella structure. And when you look at the right hand side very consistently, it also, we also observed a clear dose dependent reduction in plasma PPI, further validating the mechanism. So lots more work to do in this space, but we see again the arc here, the biology, design in vivo, with phase one expected to initiate in the second half of 2026. We see a compelling opportunity to address unmet needs in juvenile and adult onset disease, or access convenience and long-term tolerability for a chronic disease that starts early, especially. So those were the three programs where I shared a deep dive. Now I'm gonna do a quick tour, one page, or just some of the other oncology go-forward programs. So the first one, RBM39 deGrader, identified again, as I mentioned earlier, through our phenotypic platform, it mimics closely CDK12 loss, phenotypically, and induces in our preclinical models, as you can see in the middle, dose-dependent antigen activity in preclinical models. This is one place where I wanna emphasize an area of platform capability that you'll see across all of these programs, which is precision selection of patients. This is where ClinTech and causal AI efforts that we're really doubling down on in our platform is helping us accelerate for the right patients, but then also site selection and enrollment. The study right now is in phase one monotherapy, with an update expecting first half of 2026. CDK7, I've mentioned a little bit about CDK7 before, how we leverage our platform to optimize the PKPD and therapeutic index. In the middle, you see potent tumor regression. We're not showing some of the data, the clinical data from December. You can find it online, but we also sell one complete PR, confirmed PR, multiple stable diseases as well. Now what we're doing is we're using causal AI and human genetics with some of the tempest data, and then also some of the capabilities we're building in-house and cell line panels. We do both, the best of both worlds, to guide precision indication expansion with monotherapy escalation ongoing and combination, as I said, initiating first half of 2025. And MALT1. I won't go through the details of MALT1 that I shared before, but improved safety and efficacy profiles through structure-based design and hotspot analysis showed both single agent and synergistic activity. You can see in the middle, in vivo data and durable response. Or also, this is a competitive space. So we're using advanced RWA analytics, ClinTech approaches to accelerate recruitment. Already we've identified in a matter of days, 15 new high potential trial sites in the UK and Spain to support efficient, steady execution. So back to our principles from before, we can have faster PMC. And I'll wrap it up by talking a little bit about our partner programs. I know we share milestones, we share the upfronts, et cetera, but how are we? What is the nature of this partnership scientifically? These partnerships reflect tackling complex targets and generating high quality molecules across a range of diseases. And also reflect strong external validation of our approach. An additional opportunity for us to learn and create medicines, as Chris was saying earlier, and value for the company. So let's start with Sanofi. We achieved four milestones to date with Sanofi. In a partnership, it's about three years to date. That is multiple challenging targets the team is working on in both immunology and oncology. The collaboration uses the full suite of the recursion -to-end platform. In vivo biology, all the way to generative chemistry and active learning to rapidly design and optimize -in-class and -in-class compounds. I wanna emphasize what's coming up next. Over the next 12 to 18 months, we have development candidate milestones coming up, which is an important milestone, additional milestone for us, and a potential opt-in with Sanofi that starts at that base. With Roche, Recursion OS is being used. We've talked about some of the maps, but I just wanted to maybe add a little bit more color. Recursion uses Huvec maps, but Recursion has also developed a slew of disease contact-specific maps. For instance, in this partnerships, more than five phenomeps and over 5,000 transcriptomes across neuroscience and GI oncology, feeding discovery at a really fast pace. We, as you know, last year in the fall triggered a 30 million map that was accepted, and then we have more map milestones that are coming. The other piece, and Aviv Fangly calls it lab in the loop, which we really, really like, model enables not just a tight cycle of AI-driven hypothesis generation, but really important for this partnership, we're pivoting from the maps and the novel biology to now programs, right? Experimental validation and the design of the programs to have the potential to make new medicines for these insights. So I wanna wrap it up by saying we have multiple internal and external pipeline catalysts that are coming out. As you've seen, some of the internal ones are displayed on top, but then also meaningful partnership catalysts with new phenomep options, program initiations, and potential options exercised by some of our partners. So with that, I'm going to conclude the R&D and pipeline update, both internal and external. Let me close by saying that we are encouraged by the momentum received, both internally and with our partner programs, and we remain committed to a disciplined portfolio strategy that prioritizes scientific differentiation, capital efficiency, and value creation above all, the clear path to impact. With that, I will turn it over to our CFO, Ben Taylor, for the financial update.

Sure, thank you, Nita. So where we wanted to start with when we're going through some of the financials is not only to talk about the pipeline prioritization that the job just went through and personally a very high-level overview at the beginning, but also talk about how we are trying to make data-driven discipline decisions across the organization to really maximize our ability to reach all of those milestones that were on the previous slide. So if you look at how we've been adjusting our operations, not only since the merger, but even before, and trying to align that to be able to drive our cash runway as long as possible, what you can see is we've really had a focus on adjusting our capacity over our capabilities. And what I mean by that is our capabilities are the platform overall, what we can actually produce out of that platform, the capacity would be more of how many can we use. And so because we are a tech company, because we focus on automation, we actually have a great ability to adjust our capacity based on the market conditions, based on the pipeline that we want to execute, while still being able to enable all of those same capabilities across the platform. And that's exactly what you'll see from us both during the first quarter, but also through the rest of the year. Couple different points that we wanted to hit on. One, we ended the quarter with $509 million cash. We will talk a bit about cash burn, and this is something that's really important because it's a little confusing. To anyone outside of the company, looking at our financial statements to try and understand what do you actually spend in operationally to execute on all of those things that you're doing. And so what we'll try to do is take metrics and put them into the context of a cash operational burn. So how are we spending money excluding the inflows that we get from partnerships, excluding the non-cash effects. And so during the first quarter of 25, as you'll see, that was about $118 million, including all of our cash operating expenses and our capital expenditures, not including anything that was an inflow from our partnerships with financing and excluding the transaction costs as a result of our journey. So in total, we expect a cash runway into mid-2027. Now what's gone into that assumption is really based on the three different levels of how we drive cash runways. The first is thinking about our partnerships. So we've brought in 450 million from our partners over the existing partnership programs. In addition, we've also been able to hit on four milestones in the Snowkey partnership over the last 18 months. We hit on a major milestone with Roche as well. And so we're now driving towards continuing to execute on those existing partnerships and really leveraging the cash flows and the partnership milestones available to us there. We will continue to look at new business development as well and have the ability to match our operational capacity to meet those partnerships. In addition, as we've historically done, financing is another aspect that we will look at. We intend just to follow the same business patterns that we haven't passed on that aspect. Finally, what is completely in our control is our cash burn and being able to adjust our cash burn over time based on what our priorities are inside of the business. So that's where you've seen us take the steps that we have recently to be able to maximize our runway as far as possible. So if we can go on to the next slide, what you can see is a pre and a post in some ways. So on the left side of the page is the 2024. This is a non-GAAP measure, a cash burn from the two different organizations. What it breaks down to is about 600 million on a combined basis between the companies excluding all of the partnership and financing. What we were looking at for this year is a budget of less than or equal to 450 million in the same terms. How we are doing that, one, we talked about the pipeline prioritization, but we've also been able to reach deep into a number of different different corporate expenses, adjusting capacity as I talk about, and also trying to leverage the fact that we are a technology platform. We should continue to be able to do more with less because we focus on encoding and automating our processes so that we can accomplish more goals the next year than we did the previous one using less resources. You will see us continue to drive towards that every chance that we can possibly get.

With

that, I'll turn it back over to Chris.

Thanks, Ben. I wanna talk a little bit about Recursion 2.0 and the experiment that we're here to run at Recursion. You've heard from Ben and Nijat today, and on behalf of them and the entire team, we thank you for your attention. I just wanna share with all of you that we believe that Recursion will continue to lead the tech biospace. And we're gonna do that through this sustainable continued growth plan that we shared today. We're gonna remain committed to our internal pipeline, though it's gonna be more focused than it has been in the past. We're gonna continue to execute on our partnerships, and we believe there are substantial milestones that we have the potential to earn over the coming quarters and years. We're gonna continue to increase our focus on leveraging AI, not only in drug discovery, but all the way through development with some really exciting build happening in clinical development that we'll share more on soon. And as Ben just shared, we're gonna continue increasing the efficiency of Recursion while also never stopping our investment in the Recursion operating system, because ultimately, it's that operating system, that learning system that we believe will give Recursion an advantage in the coming years. And so with that, huge thanks to all of you for your attention. I think we're gonna go ahead and turn it over to Q&A. And I will start, looks like Eric Joseph at JP Morgan has asked, given your state of runway to mid-2027, what burn rate do you anticipate exiting 25 or entering 26 with? From where would you expect incremental efficiencies still to be derived, and do you plan to raise capital? For that, I'll turn it over to you,

Ben. Sure, of course. So we haven't given specific guidance on the runway, but you can imagine if our budget for this year is 450 million or less, we're targeting a runway of less than that. And so we will give additional detail as the year rolls forward. I think we're also going to continue to look for different efficiencies across the organization. Let me give you a couple of examples. We've been able to, for example, drive better contracts with our partners with a more scaled organization. We've been able to integrate different parts of the business where we had high cost on one side previously and low cost on another, potentially reach a lower cost overall. And we'll continue to drive into every aspect that we can extend that runway without de-packing our ability to execute and deliver on our pipeline programs, both internally and with our partnerships. So we'll keep driving on that. As far as raising capital, as you know, we and no one else get guidance on financing, but we plan to just really continue our previous business practices. We'll continue watching the market. We do have an ATM facility, which we have used moderately in the past. So we'll continue to use our current business practices. Thank

you so much, Ben. Next, we've got James and Joe asking a question on partnerships. When can we see an option on a molecule candidate from one of your four main partnerships and any further insights on new levers in the OS for accelerating partnership programs to commercialization? I'll take the first part of that. So we've already had four programs optioned in our collaboration with Sanofi, another program optioned in our collaboration with First Genentech. And we believe that those programs and many others coming behind them have the potential not only to get those early options, but perhaps to have the potential to go to later stage options where they might move into our partners pipelines. And obviously the economics are significantly higher at those stages. So we're continuing to do that work and we think a lot of promising progress so far. For the second part, maybe I'll turn it over to you, Najat. Any further insights on new levers in the OS for accelerating partnership programs to commercialization?

Yeah, I mean, I'll mention maybe three. You know, one on biology, we've talked a lot about the pre-nomics work that the Persian industry now adding time script only. I think the clinical genomic data that we have, it really helps you make the stack multimodal, but not just to understand holistically the biology, but also very early on start to better understand what the patient population may be. Really creating a more differentiated TPP upfront and earlier on, that's one area. The second, you know, on the chemistry and the design module, you saw some of the examples I shared for internal. It's very, very similar to what we're doing with partners in terms of can we try to model in or model out aspects that we know are challenging with molecular dynamics, QM. You'll see much more coming up in that space. And then also being able to model and predict some of the admi aspects that makes a drug more drug-like earlier on. And the third is more in development. And Chris touched on this a little bit. You know, as we partner, whether it's not just on a discovery program perspective, but also on potential partnership on an asset perspective, et cetera, we're also going to leverage some of our clinical capabilities. Again, using multimodal data, really precisely understand the patient population that we just target that would have the highest signal to noise, and then also will be more rapid in terms of how we can

do it. Thanks, Najat. Next up, we've got Vikram from Morgan Stanley, who's asking a question on the pipeline. Your pipeline prioritization leans heavily towards oncology. Do you generally see a pivot away from rare disease for your pipeline and platform? And if so, which aspects of the recursion operating system underlying approach do you think make oncology stronger fit in addition to rare disease? I think at a high level, we believe that both oncology and rare disease are fantastic areas for us to deploy our platform. In both of those areas, we have some genetic markers that often give us sort of an anchor point of biology from which to work from. And so we'll continue to follow the data. And I can imagine us continuing to drive both rare disease and oncology programs forward. The data is gonna be ultimately what drives what the balance of the portfolio looks like, but I do not see us abandoning either oncology or rare disease in the near term. Next up, we'll go to Dennis from Jeffreys, Gil from Needham, Alec from Bank of America, Brendan from Cowan, and many other folks who are asking questions around the FAP readout. And I'm gonna read these off one by one because we've got a whole bunch here, and I'll have Najat answer them. So the very first one, talk about the FAP data shared at BVW, and how has that differentiated from other programs that we may have seen in this space?

Yep, happy to do that. So when we look at the FAP data, which I just shared, efficacy valuable about NF6 patients, median polyp organ reduction in the 40s, 43%, but again, early data, right? And I also talked a little bit about the safety, where most of what you're seeing there is the on-target class effect from MEC1-2 inhibitors. The two other programs that exist, the Celcoxib, as I mentioned, used off-label, and then also another program focused on rapamycin, encapsulated rapamycin, which is in a competitor's pipeline. Both so far have shown polyp reductions 20 to 30%. So just from the primary endpoint that we're looking at. The second piece I think that's also important to note is the change in the Spiegelman scoring. And also we are encouraged by the congruence that we see polyp burden, polyp count, and then also the Spiegelman stage. Again, early data, but some of the reductions that we're seeing so far is pretty encouraging. I see there's another question in terms of the non-responders and just take that and couple it. So I'm talking through the data more holistically. Non-responder with a six-fold increase in polyps. What do we see in natural history? So in natural history, the polyp burden is increasing for these patients. But there are prior studies and one of our competitors study where about 40 to 50% of patients are non-responders in these studies. Non-responders from a polyp burden or from a polyp count perspective. And recent data has shown that even in that 40% of non-responders to polyp increase, there is anywhere from one to two to six-six increase in polyp burden. So for our one non-responder, as I mentioned earlier, we're doing a lot of work to better understand the reason for that and then the work will continue. As we have a larger end, these numbers will evolve and that's gonna be important as we look for more mature data later on this year.

The next question on FAP was will we continue to dose higher than four milligrams?

You know, what we wanna do first is we're encouraged by the reduction that we're seeing 30 to 80% polyp burden reduction as pretty significant. We wanna look at some of the data later this year, end of 10, and then next steps would be either if you need to dose higher, but then also discussions with the regulatory agencies and the potential path forward. So first we wanna complete the four milligram cohort, really better understand data, and then take next steps from

there. And then the last two, where do you see the bar for success in FAP? First in terms of FDA approval, but also as it relates to broader uptake among patients.

Yeah, I mean, that's a great question. So the bar for success for FAP, you know, what we see with some of the off-label agents that are used anywhere from 20 to 30%, there's nothing approved. So clearly there's a huge unmet need for these patients because if not, they're doing multiple surgeries throughout their lifetime. There is another agent that's in just starting phase three, and you can see some of the data, our polyp burden reduction to date is encouraging and higher, but much more to do in terms of learning about the data.

And any next steps for the program?

Yeah, so as I mentioned, you know, more patients on the four milligram by the end of this year and then conversations with the FDA on the path forward. You know, so far, endpoints have been a composite endpoint for FAP, and one of the components, as I mentioned earlier, is actually the inclusion of Spiegelman scores. So, you know, we're watching all of those different aspects and more to come, we'll see you.

Thanks, all right, next up we have Alec from B of A, who's asking, remind us how CCM, NF2 and CDIF, which are three of the programs that we just continued, were initially discovered or developed and how the refined pipeline strategy may be better reflects the current capabilities of the recursion platform. I'll take this one, so look, CCM, NF2, NFAP were all products of our recursion 0.1 platform, where we were using RNAi and tools to identify repurposing candidates. And it's exciting to see the FAP program showing us some preliminary efficacy. Our CDIF program came out of our recursion 1.0 platform, where we started to explore new chemical entities, and we see no reason today why the science doesn't continue to hold on that program. That decision today was really based on looking at the commercial landscape and the unmet need and making sure we prioritize our investment. Obviously, N are too low to draw any conclusions, but we designed recursion as a learning platform, where each generation of the platform has a higher probability of identifying and uncovering medicines that we think will have an improved probability of success. And again, the add-on recursion 0.1 will be too low versus recursion 1.0, but as we continue to learn and iterate on this platform, I'm confident that on average, the probability of success of our programs is likely to go up. That's what we're here to do.

Chris, if I could just add one point, coming in less than a year ago, just looking at the overall programs, just going back to our go-forward portfolio strategy, one of the things you'll see I mentioned is really being very thoughtful in terms of how the molecules are designed, and we can do that in-house today, and even more so doubling down on that post-integration with Exciencia. CCM and NF2 and FAPDs were all in license slash repurpose programs. So that's number one from a design perspective, from a chemistry, biology perspective, Chris mentioned as well. And then the third is also the development strategy, right? I think for a company like us, having rapid learnings, rapid -no-go, rapid and clear with endpoints that have some precedence was also gonna be important for us. So those are some of the other aspects that we're incorporating.

That's the kind of scaled learning that we're gonna get with a scaled portfolio. And in some way related, Gil from Needham is asking if we contextualize the use of AI in clinical development, like for study design, and maybe how that's relevant for programs like RBM39. I know this has been a big area of focus over the last year.

Yes, absolutely. So yeah, let's take whether it's CDK7, RBM, or even Malt1. So I'll just take CDK7 as an example. There are other programs in the competitor pipeline. Which indications do you go after? Solid tumors is very broad-sanding, RBM, same thing with others. How do you enrich the biomarker for the patient population? How do you ensure that the patient populations you're going into have certain over-expression or under-expression that is predictive of greater signal to noise? I can speak more about that, but that's one of the things that we're doing leveraging clinical genomic data like Tempus, but then also a lot of predictive algorithms that we are developing from our sunlight work that we're doing internally as well. So much more to say here. The last thing I wanna say, sometimes enrollment and recruitment gets forgotten. And also sometimes real-world data to contextualize in the open-label study also gets forgotten. Both of those are really important, not just for regulatory purposes, but also for internal no-go decision-making. How much conviction do we have in the signal to noise? So a lot of those approaches really scaling up in the last years.

Perfect. Next up, we have Melissa who asks, what criteria were used to prioritize certain programs over others and how does this focus advance or alignment recursions long-term strategic objectives? Please.

Yeah, I mean, in terms of the criteria that we use is very much what is best in class in industry. So first and foremost, it always starts with what is the potential value of the drug? Patient population, need, scientific data starting from pre-clinical, clinical data, competitive differentiation, et cetera. Also in terms of the development plan, is there a feasible development plan? And then we look at risk, which is the other side of the coin. So for each program, look, if I can coin it in one sentence, taking all of those components and we've done our computational approach bottom up so we're not being objective or we're being objective in terms of the decision-making comes down to, do you believe this can be a differentiated medicine and is it serving an unmet need that will exist by the time you are going to be in the market? That's the most important. That bar has to be very, very high for us.

Perfect. And the final question, Brendan from Cowan asks, do you expect any meaningful impact your internal partnership strategy in light of the FDA's updated animal testing guidance? And I'll take that one to end us. But recursion was built for an evolving regulatory framework like the one we're seeing from the FDA and we'll continue to monitor for additional updates as the FDA explores all the ways that AI and other tools can be used. But from our discovery platform to our predictive ADME platform to even our in vivo platform, we are generating large scale data sets. We're building foundation models that are allowing us to move from a test at scale in the lab sort of regime for preclinical studies to a predict and validate regime for preclinical studies. And so I think recursion is not just positioned to take advantage of these regulatory updates but actually positioned to lead in the space going forward. So with that, we appreciate everybody's deep attention. Thanks to everyone for joining and we look forward to seeing you all out on the street. Thank you so much everybody. Bye bye.