Recursion Pharmaceuticals, Inc.

Good morning, everyone, and thank you for joining us. Before we begin, I'd like to remind everyone that today's discussion will include forward-looking statements. Next slide. Please refer to today's press release and our SEC findings filings for additional details. At Recursion, our mission is to decode biology to radically improve patient lives. and we do this by building transformational medicines with an AI-native product engine. Over the past year, we have reached an important inflection point. We are no longer just discussing the potential of our platform. We are demonstrating the ability of our AI-native product engine to generate differentiated programs and medicines. Just as a reminder, the engine you see on the left hand side is built as a continuous learning system. proprietary multimodal data created in our data factory powers frontier AI models. And these models then generate new hypotheses where every single prediction is tested experimentally. Each cycle strengthens both the engine and the products it creates. Ultimately, though, the measure of any engine is its output. So let's talk about that. First, our internal pipeline continues to mature. We now have five clinical stage programs, including REC 4881 and FAP, where we have generated some of the most promising clinical data in the company's history. Remember, in a disease with no approved therapies and a TAM of almost 10 billion. Second, we continue to make significant progress in our partnerships while learning from the best in the industry and also while validating our engine externally. Together with leading biopharma partners, we have generated more than 500 million in realized inflows while advancing differentiated programs with Sanofi and Roche Genentech. So today I'll share how we continue to strengthen our product engine and how we take these advances and are translating it into differentiated medicines, differentiated partnerships, and ultimately better outcomes for patients. So the question that naturally comes up, what makes our product engine different? There are many companies applying AI to drug discovery. We believe our advantage isn't AI alone. It's the combination of three capabilities that reinforce one another. First, we generate our own proprietary multimodal biological and molecular data at scale. This matters because AI can only learn well from high quality data. and much of the most valuable biology has never been measured systematically. Our 50 petabytes of data is designed specifically to train models, discover new biological relationships and improve over time as new algorithms emerge. Second, we connect these models directly to experimentation through a lab in the loop system, spanning biology, design and increasingly the clinic. Every prediction, as I mentioned before, is validated experimentally. Every result feeds back into those models. It is that recursive loop that helps us to move faster, improve our decision quality, and systematically build confidence in our programs. And third, and most importantly, we convert these capabilities into differentiated assets. That includes both our internal clinical programs, such as REC 4881 and FAP, REC 1245, RBM 39, and solid tumors, as well as our partnered programs with Sanofi, Roshan Genetics. So how are we doing? Let's look at the progress we've made over the year to date. As we look back over the first half or so of the year, I'm very pleased with the progress we're making across all three dimensions for business, our internal pipeline, our partnerships, and the continued advancement of our AI-native product engine. On the internal pipeline, we advanced REC 4881 with our initial FDA engagement, following encouraging phase two data and additional phase two data coming later this year that Vicky will talk about shortly. We have continued to build confidence in REC 1245 with early clinical safety and pharmacokinetic data. And we just received IMD clearance for REC 7735, positioning it to enter the clinic later this year. At the same time, our partnerships are also making progress. As you'll remember from earlier this year, we achieved another milestone with Sanofi, our fifth to date, on developing a novel lead series for a very challenging first-in-class oncology target. But I'd like to pause on a new milestone in particular that we're announcing today. Together with Roche Genentech, we are thrilled to announce that Genentech advanced the collaboration's first neuroscience target, a new unexplored target in neuroscience, into a joint early discovery program, providing early evidence that Recursion's platform can generate novel, biologically validated targets for drug discovery. To me, this represents much more than another partnership milestone. in an area where progress has been slow for decades. It provides early evidence that a fundamentally different approach, combining proprietary disease-relevant atlases, purpose-built foundation models, and that rigorous computational and experimental assays that we use to build confidence that these targets are actually causal. And of course, last but definitely not the least, the deep collaboration, scientific and technical, with the partner can uncover previously unexplored therapeutic targets. While it's still early, I believe this is an important proof point for both recursion and the broader field. It suggests that an AI-native engine can move beyond optimizing known biology to discovering new biology, compelling enough to advance into drug discovery with one of the world's leading neuroscience organizations. So that's just the left-hand side, but we have a lot more coming ahead. For REC 4881, we will present additional phase two data at the CGA IGC Conference, a premier medical congress for inherited GI disorders, our specific target audience for FAP. And we will also provide an update on our FDA interactions, as well as continue advancing what we believe could become a transformational therapy for patients with FAP. Remember, nothing approved to date, no approved therapies. For REC 1245, we are continuing our dose escalation and generating additional phase one data, and we'll have a more wholesome update later this year. With Sanofi, we expect the potential nomination of an oral INI development candidate, a very important milestone that would further validate our ability to design differentiated small molecules against challenging targets with the potential to impact multiple immune mediated diseases. and finally, we expect to initiate the phase one study for REC7735, further expanding our clinical oncology pipeline with another precision design program from our engine. Taking together these milestones reflect a company that is delivering ambitious proof points that matter while executing with focus and discipline. But equally important, we continue to strengthen the engine itself. Let me show you a few examples of how that innovation across biology, chemistry, and clinical development is making our engine faster and smarter. Let's start with biology. One of the biggest challenges in the industry is that much of human biology remains unexplored. We believe the answer isn't simply building larger AI models. It's generating proprietary disease-relevant data that these models can actually learn from. To do that, we have generated and aggregated more than 50 petabytes of multimodal biological data, creating what we believe is one of the largest proprietary datasets in the industry. And as that dataset grows, our models become better at discovering novel biology, and every new discovery further strengthens the engine. That learning then carries into design. Because our biology models generate higher confidence hypotheses, our chemistry platform focuses on designing better molecules more efficiently. There's much to share here, but one thing I'll mention is we are advancing candidates using roughly 330 compounds over approximately a year and a half. So going from target to candidate in a year and a half, compared with industry benchmarks for small molecules of roughly 2,500 compounds over four years. That's a meaningful improvement in both speed and capital efficiency. And finally, we extend that same philosophy into the clinic. The clinical development is where a lot of value is ultimately created and where also a lot of programs fail. By bringing AI into trial design, picking the right patients that can't enforce that enough, and Site Selection, we're already seeing improvements in enrollments, speed and patient matching, helping us to run smarter and more efficient studies. But one more important point, this isn't three different capabilities, it's one continuous learning system. Every experiment improves our data, better data improves our models, better models make better molecules, and then clinical data is fed back into the system to make the next generation of products even stronger. Perhaps the best example of the flywheel in action is what we have demonstrated with Roche Genentech and we're announcing today, where our biology engine discovered a previously unexplored and new neuroscience target. I'd like to spend a few minutes just to take you behind the scenes as to how we got there and why we believe this represents an important new approach to discovering medicines. Together with Roche Genentech, As we worked in this area to discover a new unexplored target from our AI driven map of biology, we focused on a few specific elements. Why does that matter? First, this wasn't about finding another target within a well studied biology. It was about uncovering previously unexplored biology and building enough evidence experimentally to advance it into drug discovery with one of the leading neuroscience organizations in the world. Second, we believe this validates something bigger than a single target. It provides early evidence that when you combine the right data, build the right models, do very rigorous computational and experimental validation, and pair that with the right complementary collaboration, you can actually systematically uncover novel biology. And we believe this is just the beginning. The underlying biological maps are reusable. This is a really important point, with the potential to generate many more therapeutic opportunities over time. Finally, across our collaboration with Roche Genentech, we've now achieved more than $216 million in upfront and milestone payments, with the opportunity for more than $300 million in additional development, commercialization, and sales milestones for each future small molecule All right, so let me show you how we built this engine. To understand why this milestone matters, the question is why neuroscience? It's worth stepping back and asking that question. Neuroscience remains one of the greatest unmet needs in medicine. More than 3 billion people worldwide are affected by neurological diseases. And yet, CNS drugs, as we know, continue to have amongst the lowest approval rates in industry. Neuroscience is particularly challenging because the biology is extraordinarily complex, difficult to model, and we have repeatedly returned to the same small set of well-understood targets with only incremental success. We believe meaningful progress will require discovering new biology, not just simply optimizing what is already known. And that's exactly what this collaboration was designed to do. So the next question comes, what does it actually take to discover a target that people will have confidence in? And before I go into the details, just a huge, huge thank you to Roche Genentech for this deep shoulder-to-shoulder collaboration. It's one of the few rare ones that I've seen where the teams are looking at the same data, talking about the same models, going through what validation needs to be done. So that joint collaboration was critical here. Everything starts with disease-relevant biology. We asked ourselves a simple question. Are we studying neurons in a context that actually reflects human disease? In our case, that meant creating iPSC-derived neuronal cells, both neuronal and microglial cells, at an unprecedented scale, more than a trillion neurons and hundreds of billions What this does is it creates a rich disease-relevant atlas that can be reused again and again to discover multiple future targets. We view this atlas as one of the most important long-term competitive advantages. But generating proprietary data, while important, isn't enough. The next challenge is making sense of it. Before asking the models to find something new, we grounded every analysis in causal biology that we understand today. So really grounding it in genetics. We introduced hundreds of disease-causing perturbations and anchored our searches around well-established drivers of neurological disease. That matters because it gives every subsequent prediction of biology from a causal target from the very beginning. Rather than searching blindly across the genome, we are searching from a foundation grounded in causal genetics and disease biology. Now, as that's established, AI can help us on our foundation models ask a much more interesting question. What is not seen? What can be unexplored biology that we don't know of today? This is where our foundation models come in. Instead of evaluating one hypothesis at a time, the models compare the biological signatures of more than 17,000 genes across tens of millions of data points. They build relationships across the entire genome and identify genes that consistently behave like known disease drivers, even if they've never been implicated in that disease before. That allows data and foundation models, not preconceived hypotheses, to compile a prioritized list of new novel potential targets. Now, AI can generate hypotheses, but medicines and programs require evidence. Together with Roche and Genentech, we predicted every target. We looked at every predicted target and then put that through a rigorous experimental validation cascade. We build confidence in layers. First, we establish that the target actually sits in the right biological pathway. Second, we show that changing the target actually can improve cellular function, for instance, neurons or microglia. And finally, very critical, we demonstrate that this target and modulating it can meaningfully affect disease-relevant biology using multiple orthogonal assays. These assays are very robust, but they also include other multi-omic data layers, such as proteomics, transcriptomics, et cetera. While no single experiment tells the story, what we do here is build a body of causal evidence before advancing the target. So putting it all together, our collaboration combines four capabilities. Generating disease-relevant biology at unprecedented scale, and it's challenging to do, to actually have a trillion iPSC-derived neuronal cells that are high quality, standardized, viable. It takes a lot of specialized protocols and know-how to do that. Second, we use foundation models to systematically explore that biology. Third, we navigate from well-understood disease mechanisms towards previously unexplored new biology. and finally, a very important step is validating all of these predictions experimentally before we advance it. So our first neuroscience target, as I mentioned before, has now advanced into a jointly developed small molecule discovery program supported by our design platform. And again, what excites us most is of course this target, but the fact that this kind of data is highly reusable. The potential to mine it over and over again for unexplored targets, and also that this wasn't the result of one algorithm or one experiment. It's the result of a new operating model for discovering medicines. Before I hand it over to Vicky, I would like to highlight as we move on to our internal programs, the pipeline. As you can see here, we have multiple programs in the clinic. We're constantly looking at the data to make data-driven decisions. For REC 4881 and FAP, where there's no approved therapies today, and REC 1245 targeting RBM, a novel first-in-class target, first-in-class degrader with limited clinical competition to date. Combined with additional internal and partner assets, we believe this creates a diversified portfolio with multiple opportunities to create value in the coming years. With that, I'm going to turn it to Vicky to walk you through the internal pipeline in more detail.

Thank you, Najat. I'll start off this morning by talking about our REC 4881 program in FAP. FAP is a rare disease that requires lifelong management. Patients with FAP develop hundreds to thousands of adenomatous polyps in their GI tract and require colectomy to reduce the risk of colorectal cancer. Following colectomy, polyps may continue to develop and grow both in the residual lower GI tract as well as in the duodenum in the upper GI tract. Patients require ongoing endoscopic surveillance, may require additional surgeries, and they continue to be at risk for GI cancers. With over 50,000 post-colectomy patients in the US and EU5, there are no approved systemic therapies to alter the course of disease. This represents an over $10 billion potential addressable market. REC4881 is an oral MEK12 inhibitor with a differentiated dual mechanism of action in FAP with the potential to inhibit both new polyp formation via crosstalk inhibition of the beta-catenin pathway, as well as to directly interrupt signaling of the MAP kinase pathway, which is a key signaling pathway in advanced disease. So again, blocking potentially both new polyp formation as well as the existing polyps within the GI tract. So with that, I'd like to take a minute to discuss the impact of this disease on patients through a story of a woman named Jenny who lives with FAP. Like approximately 70% of FAP patients, Jenny inherited the genetic mutation responsible for FAP from a parent, in her case, her mother. Seeing what her mother experienced had profound psychological impacts on Jenny, who knew from the young age of eight that she also carried this mutation. She has since had to endure multiple surgeries, which have led to chronic and life-altering complications, including frequent bowel movements, malabsorption and dehydration, chronic abdominal pain, and anxiety with medical PTSD from all of the surgeries and procedures. We have heard from both patients like Jenny, as well as their treating physicians, an interest in a pharmaceutical intervention that can prevent polyp growth and disease progression and ultimately lead to a reduction in the need for repeat surgical procedures. REC 4881 has shown promising clinical data in the ongoing phase two, two below study. Patients who had undergone colectomy for FAP receiving 4881 showed a median polyburden reduction of 43% after three months of treatment. That treatment effect was durable with sustained reductions after three months off treatment. Additionally, reductions in polyp burden were seen in both duodenal disease in the upper GI tract as well as the lower GI tract. The upper GI tract in particular is an area of high unmet need as approximately 90% of FAP patients will develop upper GI polyps. When removal of these upper GI polyps becomes necessary, the thin mucosal wall of the upper GI tract increases the likelihood of complications, including bleeding and perforation. REC4881 has a manageable safety profile with predominantly mild to moderate adverse events consistent with the safety profile of other MEK inhibitors. We continue to enroll patients on the Phase 2 Tupelo trial, including patients 18 years of age and older, as well as a dose optimization cohort. We are pleased to share that additional REC 4881 data will be presented during the Presidential Plenary Session at the CGA IGC Conference in November. As Najat mentioned earlier, this conference is focused specifically on inherited GI cancer syndromes with a target audience which includes physicians who treat FAP patients. We also look forward to providing an update on FDA discussions later this year. Now I'll move on to REC 7735. PI3 kinase is frequently mutated in several cancers and is a clinically validated therapeutic target. Lack of selectivity for the mutated form over the wild type is a key challenge for existing agents as inhibition of wild type PI3 kinase drives hyperglycemia. Increases in blood glucose are both a safety issue, which often limits dosing, and an efficacy issue, as the resulting hyperinsulinemia can reactivate signaling through the PI3 kinase pathway, undercutting the efficacy of less selective drugs. REC7735 is precision designed to be 100-fold selective, greater than 100-fold selective, for the H1047R mutation. which is the most frequent activating mutation in PI3 kinase. Recursion's AI native platform identified a previously unpublished binding site and delivered a development candidate in 10 months with no identified off-target liabilities. As hyperglycemia and the resultant hyperinsulinemia are driven by inhibition of wild type PI3K, The selectivity of 7735 is expected to result in an improved safety profile with respect to hyperglycemia and may allow expansion into patients such as diabetic and pre-diabetic patients who are unable to tolerate current PI3 kinase targeting options. An improved therapeutic index, as I have described, may allow us to expand treatable patient populations both within existing PI3 kinase alpha inhibitor indications, as well as in additional solid tumors in which PIK3CA mutations are prevalent, including potentially triple negative breast cancer, ovarian cancer, and endometrial cancer, just to name a few. Additionally, the improved therapeutic index may allow expansions into earlier stages of disease within oncology as well as non-oncology populations such as PI3 kinase-driven vascular anomalies. With the IND now cleared by FDA, we intend to initiate a Phase 1 Zinnia trial later this year. Dose escalation will begin in patients with PIK3CA H1047R mutant solid tumors. Once tolerability is confirmed at an active dose, we intend to expand into the hyperglycemia vulnerable patient cohort to confirm the improved tolerability in this patient population. Dose optimization of two active and tolerated doses will then be performed in ER-positive HER2-negative breast cancer patients. We may also expand into additional tumor types based on emerging data. We expect to share the first data from this dose escalation part of the trial in the first half of 2028. And with that, I'll turn it back over to Najat.

Thanks, Vicky. And shifting gears a bit, we often get asked about whether advances in frontier AI can reduce or increase recursion's competitive advantage. We believe we have a truly competitive, unique competitive edge. As reasoning models and agents continue to improve, next slide, They become dramatically more powerful when paired with proprietary data, automated labs, and real experimental feedback. That's exactly the system we've been building for years. Now, we are deploying agents across biology, chemistry, and clinical development across the engine and also alongside our scientists. In biology, here's some very quick examples. Our target discovery connector is helping scientists interrogate our proprietary biological maps in hours rather than weeks. These are the large maps that we just talked about earlier in our partnership with Roche Genentech, but also the internal maps that Recursion has built over years, accelerating the discovery of novel targets. In chemistry, our design agent reasons across structure, SAR, and experimental data to prioritize the next design hypothesis, critical inflection points in programs. This helps our scientists decide what to make next and compress design cycles from roughly four hours of structural analysis to about 30 minutes. And in clinical development, the agentic workflows are already improving patient enrollment, contributing to about 1.3 to 1.6 fold improvements over historical benchmarks. That's significant. These are still early examples. But I will have Chris Redoux, our Director of Structure-Based Technology, who's in this day in and day out, walk you through a real example in practice. Chris?