Hi, everyone. Let's get started. Welcome to the 44th Annual J.P. Morgan Healthcare Conference. My name is Priyanka Grover, and I'm part of the J.P. Morgan Biotech team. Today, our next presenting company is Recursion, and presenting on behalf of the company is CEO Najat Khan. Thank you. Thank you, Priyanka. Good morning, everyone. It's a pleasure to be here today. It's a very exciting time for Recursion in terms of the recent momentum that we have and also our path ahead. So, as I walk you through some of these slides today, I'm going to focus on and walk you through three specific topics. First, how we're doubling down on translating the insights that we see into proof points that truly matter. And we're just coming on the back of our first platform-enabled clinical proof of concept. So, I'll share more about that. Second, we're also going to focus on how we are surgically doubling down on certain areas in the platform that are grounded in impact. They really focus on the bottlenecks that we see in R&D. And third, we're pairing that big ambition with discipline. Discipline in our execution, discipline in our financial stewardship, and also discipline in how we operate. So, with that, let's dive in. Before I get started, please note the forward-looking slide, forward-looking statements on the slide. All right. So, Recursion's mission is bold, and it's also very patient-centric. Our mission remains unchanged: decoding biology to radically improve lives. Two things to note here. First is patient focus. All of our decisions and our investments are focused on making better medicines for patients that matter. And second, we also talk about decoding biology. Biology is one of the most unknown areas, and therefore, to decode biology, we needed to take a fundamentally different approach to discovering and developing medicines. To do that, what we have built is a unified AI-native intelligence platform that translates complex science, that unknown science and biology, into medicines that truly matter. and we focus on medicines that are better, that we do it faster, and at scale, because patients are waiting. You know, we often get asked that question: what makes Recursion different? It is not one data set. It's not one asset. It's not even one model. What makes us different is the fact that we have built proprietary multimodal data at scale. This is a huge gap in the field today. We've done that with our wet and dry automated labs in Salt Lake City and London, Oxford. In addition to that, the models that we built are purposeful. They are focused on the questions that actually address making better medicines. And a point that doesn't get appreciated enough is our talent. I've said this for a long time: bilingual teams and cultures, scientists that understand AI, and AI scientists that actually understand science. That's incredibly hard to do, but it's core to unlocking our differentiated data, models, and compute. The other piece that I want to spend some time on is how this comes together. That is incredibly different. We are the first AI-native company that has an end-to-end platform from biology, chemistry, all the way to clinic. Now, you may ask, why is that important? For many of us that have actually made drugs, we know that you have to improve decisions at every part of the R&D value chain. It's insufficient to have a better model in biology but not to be able to execute on that trial in the clinic. We use AI to advance and accelerate decision-making and insight across the entire value chain, with the end goal, only one end goal, to make novel medicines that matter. So, what are we working on? Let's talk a little bit about Recursion by the numbers. We have five clinical programs in our portfolio. And as I just mentioned, we had our first AI-enabled clinical proof of concept with a potential first-in-class profile. This is a disease where there are no approved pharmacotherapies. We also have 15 programs in discovery. And I just want to point out both partnered and wholly owned. But on the partnered front, we have pulled in over $500 million in upfront and milestones. What does this mean? Momentum. Clear, tangible value being created. And each of these programs that we're working on with our partners, whether it's Roche, Genentech, or Sanofi, have an average of over $300 million in potential milestones, with up to double-digit royalties per program. In addition to that, we also have $755 million in year-end 2025 cash, providing an expected runway until the year-end of 2027. We have done a lot of work operationally to get there, and I'll get into that more. So, a question we get asked often with this new chapter in Recursion: what are going to be the priorities? First and foremost, doubling down on translating insights to proof points that ultimately become new medicines. That comes from our wholly owned portfolio, and that also comes from our partnered programs. I'll double-click on that more. Second, we are going to be surgically focused in terms of innovation in our platform where it makes a difference, taking insights, connecting patient data to ensure that our insights are actually actionable. We've also built a clinical development AI platform that is very new in this industry. And then the third area of focus is pairing bold ambition with disciplined execution. And underpinning all of that is our exceptional people. So, let's dive into this a little bit more. We had our first clinical POC with 4881. We saw meaningful, rapid, and durable polyp reduction with a safety profile consistent with our class. Where are we going next? The next step is to align with the FDA on a registrational study plan. In addition to that, we have five more programs coming with clear go/no gos and differentiation derived from our platform. We also have external validation of our platform from our partners. Four milestones achieved with Sanofi, where we're taking hard targets and designing small molecules using AI. With Roche Genentech, we have delivered six AI-powered Maps of biology. Remember the point on unknown and unknown biology? We're changing that. So, what to expect next? For the programs that we have with Sanofi, taking that and progressing it further into the value inflection points, later-stage discovery milestones and development candidate. Then for our Maps, taking those Maps, taking the novel insights, and converting them into new partner-accepted discovery programs. In terms of our platform, we have been able to drive unprecedented scale in phenomics. Now, we're layering in omics data and patient data to drive high-quality targets. In our chemistry and clinical development portfolio, we are going to go full-on at scale. In the third pillar, in terms of pairing bold ambition with disciplined execution, we have done a lot of hard work here. We prioritized the portfolio only six months ago and already have our first clinical POC enabled by our platform. We also streamlined our operations significantly. Expect to see more of this. Clear go/no-go decision, clear discipline investment in our platform, and leveraging agentic agents to really improve not just how we make decisions on our programs and our platform, but just how we operate as a company. So, expected to 2026 cash burn of less than $390 million. This is a 35% reduction in pro forma since 2024. All right. Now, I'm going to double-click on some of these proof points. So, let's start with our wholly owned clinical pipeline. I'll double-click on our FAP program, which is REC-4881. But just to say, in addition to that, we have several programs with clear go/no- gos, which fall under two themes of differentiation. One, novel targets that have never been studied before, such as RBM39 in solid tumors, or in fact, REC-4881 as well, where the insight of MEK1/2 inhibition and the loss or the rescue of APC was not known, then the other theme that we have are targets that are important but challenging from a therapeutic index perspective. This is where our chemistry AI design platform comes in, so examples such as LSD1, CDK7, and more. Every single one of these programs leverages our platform. I won't go into it in detail, but it shows you how much the platform has evolved across biology to insight, insight to molecule, and molecule to patient, and we leverage and track how we use our platform across every single program. Sometimes it's focused in on novel biological insight, sometimes it's on design, and sometimes it's both, so let's dive into FAP. Lots of words on the slide, but three important points to note. FAP is a disease with significant unmet need. Over 50,000 patients, US and EU, no approved pharmacotherapies. Surgery and debilitating surgery is the current standard of care. It's a progressive lifelong disease, starting in adolescence throughout the entirety of your life. Our platform derived a novel insight where we actually used our phenomics approach to knock out the gene that actually drives FAP, APC. The images of cells with an APC knockout versus that of healthy look different. This is where foundation models come in. We screen thousands of compounds and, in an unbiased approach, learned that this allosteric MEK1/2 inhibitor did the best job of reversing disease to healthy. We then licensed this asset from a pharma company. The other piece that we also use our platform is our new clinical development AI platform. This is a rare disease with no approved therapies. We wanted to better understand the natural history and progression of the disease. Very important to power studies, but also in regulatory conversations. We built the largest, longest-running registry with the Amsterdam University Medical Centers to understand that these polyps that are the hallmark of the disease, remember, every single polyp is precancerous with a 100% risk of CRC, colorectal cancer, if not treated. They don't spontaneously go away. In fact, they progress in a relentless fashion, which is why many of these patients get multiple, several surgeries a year, and we learned that also in the U.S. We built an LLM in under a week, and we're able to scan across 260,000 U.S. physician notes to really understand and contextualize the current standard of care. This is what modern clinical development looks like, and let's talk about the data. From a safety perspective, in line with what we see with MEK1/2 inhibitors, majority grade 1-2, derm and rash being some of the main drivers. In terms of our efficacy data, 75% of patients responded. Rapid reduction in polyps in three months, 43% median. This is above and beyond what others have seen before, and what was even more exciting and a little surprising is the durability. When these patients are on drug for three months and then they're off drug for three months, 4 mg QD, the polyp reduction is sustained. That is incredibly important for a chronic disease. So, what's next? I love this arc. We go from insight to proof points and defining the registrational path. That's the core next step with the FDA, and we continue to optimize our dose, looking at broader age ranges, et cetera. All hands on deck. Next, let's talk about our partner discovery. This is another core pillar for us in terms of our proof points. Look, we brought in over $500 million in cash flows and a very fast momentum in terms of milestones that you can see here. But I want to uncover that a little bit more. So, let's talk about Roche Genentech. We often get asked the question, what is a Map? Well, let's talk about it. You know, for Recursion to build these Maps, this proprietary data that doesn't exist, these are two examples in neuroscience. Recursion in our Salt Lake City labs, we culture and develop over a trillion iPSC-derived neuronal cells or 100 billion microglial cells. We then, and the wet lab work continues, do perturbations across the board, the entire genome, overexpression to be able to replicate some of the disease states that are important. And that's when our dry lab kicks in. We build foundational models, AI models, to be able to drive insights from those Maps. That's what a wet, dry lab driving insight really looks like. And what's the focus for us next? We've actually delivered more than twenty-six of these Maps with multiple milestones coming from Roche Genentech. The next step is to drive those insights into novel programs. This would be a first in this industry. And we're working hand in hand with our great collaborators at Roche Genentech in order to do the functional validation, truly translate them into programs. Let's move to Sanofi. In Sanofi, we're using the chemistry design AI segment of our platform. Here, we're working on incredibly challenging targets, targets that we've worked on for a long time unsuccessfully. And we're designing molecules using our AI platform. This is where novel approaches, this is where the investment in the platform is critical. You know, a lot of these projects are data poor, using active learning approaches in order to make progress. So, we have achieved four programs, milestones to date, and more late-stage discovery milestones coming up. So, stay tuned. All right. Our second pillar was being incredibly judicious and surgical in terms of where we invest in our platform. This space is moving fast, and we want to ensure we're investing in areas that are grounded in impact that get us to more of those proof points that we're all waiting for. So, let's start with the biology side. We talked about phenomics, incredibly data rich. What we want to now do is to layer on more omics data. In a matter of months, the team built the state-of-the-art transcriptomic models that is now enabling us to connect our lab insights to patients' translation. This is incredibly important as we think about novel targets. Why are novel targets important? Remember, that's what generates our first-in-class programs. This is generalizable across many different data sets: single-cell, bulk seq, patient data, in vitro data. When you build good models, they tend to be very data efficient, 50x less training needed. More work to do here, but exciting progress. Next, let's talk about our chemistry engine, AI engine. We have doubled down here significantly. You saw some of the milestones from Sanofi. You've seen some of the programs we have in our clinic and actually in discovery as well. We have generated over 100 million molecules. What's really important is these are synthetically aware. They're not esoteric structures that you can't actually make with a partner or a CRO. And over 95% of these compounds are not handmade. They're AI-generated and prioritized. On average, just to give you a metric, we're synthesizing about 330 compounds to get to advanced clinical and advanced candidate in about 17 months. The benchmark? 2,500-5,000 in 42 months. That's how we're trying to do things faster as well. And then this is our newest part of our platform. You know, 70 cents on the dollar in R&D to make a drug is actually spent in clinical development. And everybody spends a lot of time talking about discovery. We want to win in all parts of the value chain. So, we built our AI-driven clinical enablement platform, where first we pulled together the data model and the data foundation, over 300 million patient lives. And we have two main areas we're focusing on. One, how do we pick the right patients and the right indications for our programs to really improve the signal-to-noise? And the second, how do we just execute flawlessly? How do we run these programs fast? And you're already seeing some great wins there in terms of 10%-40% increase in some of the eligible patient populations. So, we have a more unbiased approach to protocol development based on actual real-world data versus assumptions, and then also an increase in enrollment rate. Much more to come, and over the coming earnings, I'll be sharing more details about what we're doing here. This was just a quick flash. So, looking ahead, we have a wide range of upcoming milestones. So, starting with our wholly owned portfolio, you know, REC-4881, engagement with the FDA, and starting those conversations, the first half of 2026 is going to be very important, with more conversations coming up to align on our registrational path study. Second, we also have our monotherapy early safety and PK data for our RBM39 program. We also have some go/no-go decisions for programs that are entering the clinic: PI3K, 1047, mutant selective, and also ENPP1. And for next year, we also have additional data we expect from our REC-4881 program as we do different dose schedules and then also the age, the broader age label. In addition to that, we also expect some combo data from our CDK7. Recall, this is in combo in second-line platinum-resistant ovarian cancer, and then also monotherapy dose escalation from MALT1 and LSD1. In parallel, significant partner milestones that we're also focused on that I mentioned before. And to round it all out, as I mentioned, $755 million in year-end 2025 cash with an expected runway until the end of 2027. And also a cash burn that we're watching closely so that every dollar goes into value creation around our proof points. So, with that, thank you for your time, and looking forward to your questions. All right. Thank you so much for the presentation. So, I'll ask the first couple of questions, and then I'll ask the audience. So, feel free to raise your hands, and they will get a mic to you shortly. So, just my first question happens to be on the REC-4881 program or FAP. What do you think are the underappreciated points of data from the REC-4881 December update? Yeah, thank you, Priyanka. Great question. Look, I think there are three main themes of underappreciated area. I think one is really around the unmet need. You know, I touched on that, but maybe to go into it a little bit more, this is not a rare, rare disease, 50,000 plus U.S. and EU patients. But what's more important is the lifelong nature of this disease. You know, most patients start in adolescence with hundreds of polyps in their colon. The majority of them end up getting a colectomy in their 20s or 30s. That is a huge quality of life impact, as you can imagine. And having spent some time with physicians to treat these patients and their patients themselves, the questions are very consistent. How can I delay surgery? How can I prevent surgery? Next, 90% of those patients end up, even after you get a colectomy, the polyp keeps growing in your rectum and upper GI, so you get your rectal pouch removed. And then, over time, sometimes you also get a Whipple procedure. So, just the progressive nature of the disease and the debilitating surgeries, I think that's not fully appreciated. And this is why, and the fact that there's no approved pharmacotherapies, there's a huge unmet need for these patients. So, that's point one. I think point two is sometimes the novelty of the insight from our platform is also underappreciated. You know, there was no prior knowledge that MEK, like a MEK kinase inhibition, would actually reverse what you're seeing with APC loss. That was not known. It was never studied in the clinic. And I think that is something that's incredibly important to double down. It gives you first-mover advantage to actually have novel insights, and you can do that at scale, and then the third, look, from a data perspective, there have been other studies in FAP before, but the fact that we see such rapid response in three months, pretty significant from what others have seen, and the durability, given the relentless nature of this disease, I think are very important points to keep in mind. We have a lot of important work to do with the FDA and regulators and so forth, but we are excited and all hands on deck. So, you touched on this, but in 1H26, you'll engage with the FDA to define a potential registration pathway, and you'll include real-world evidence assessments. What findings from these real-world evidence assessments would you have the street focus on? Yeah, I mean, so a couple of things. So, first of all, we'll have our first set of preliminary conversations with the FDA first half of 2026, with more conversations after that to refine and finalize. This is where I think the FDA has provided a lot of guidance in terms of real-world data. If you look the last 18 months or so, and even before that, I would say, it really helps to contextualize, especially for a rare disease, especially for a disease that there's no approved therapies, in terms of what's the natural history. We're not talking about a couple of literature articles. These are, as I mentioned, the longest and largest running registry, over 200 patients over 20 years, which helps us also quantify and understand, you know, elements of how to power the study and so forth. So, I think form of supplemental contextualization is going to be very, very important, and something that the FDA has encouraged for rare diseases and, quite frankly, also in oncology, and the analysis and the study meets those specifications as well. So, you know, for the last decade, we've talked about more integrated evidence generation in our clinical programs. I think this is an example of that where you have real-world data to contextualize and, of course, clinical data for efficacy and safety. Any questions from the audience? Yes, please. First of all, I want to ask two questions. Yesterday, we also saw that NVIDIA was working with Lilly to build their AI labs. So, there is a real obvious trend that the MNC is trying to learn AI from the discovery, and the AI companies are also learning how to do the clinical trials and potentially sales and distribution. So, these two models, which one do you think would be easier to pick up? That's the first question. And then secondly, we also observed another trend that, like so many AI companies, in order to generate cash flow, they are partnering with the MNCs. So, in terms of investments, if the best assets are being partnered with the MNC from an investor perspective, like, should we invest the MNC or should we invest in the AI company and why? Thank you. You've clearly thought about this for a long time. Great questions. Look, in terms of the NVIDIA and Lilly announcement, I think this is, we think it's great for the industry. I mean, there's been so much talk about AI, but no one really doubling down to make the investment that it takes. I think it also says a couple of things. Number one, the need for actual high-quality data generation, the need for a wet and dry lab, which Recursion has been a pioneer in, that is actually playing out in terms of having a lab that can do both. The second part that I think is also important is it sets companies like Recursion that were ahead of the curve and also invested early in terms of having that data moat that's needed to be able to capitalize on this trend. I mean, AI is not going away. It's really around how and who and what value creation you do. You know, we were at the event yesterday with NVIDIA, with Jensen, and we just got awarded a Spark Award from him. Just, you know, he said something that was really important that often, often your audience or your market might not even know what's the need, and that's really how NVIDIA started. And I think you're starting to see a progression and acknowledgment of the investment that's needed in the space, both wet and dry lab. And I think you said MNC, but I'm going to take it as pharma companies. Pharma companies, whether it's platforms, AI, or even ADC and others, there's always a build and partner approach. It's usually been an end. There is so much white space when it comes to biology and chemistry in the space. So, again, our focus is going to be to continue creating value. That's what's going to set us apart. That's why you see our first pillar is translating those novel insights into proof points that matter to make medicines. So, that's your first question. Second question, as you were asking, you know, in terms of cash flow, I think you said that some of these companies might be partnering some of these programs. I think you have to be really, really judicious in terms of which partner, which programs you partner versus not. We're open to both approaches, but some programs might do better in our hands, and in some programs, which, you know, our partners could actually accelerate some of what we're doing, we're very open and agnostic to that. Yeah, maybe just a couple points to add on to that. One, I think it's a common misunderstanding. We don't change the what. We change the how. And so, we're still developing medicines for patients. We may be able to do the discovery in a different way. We may be able to do the development in a different way. In the future, maybe we do the commercialization in a different way. But it is the how, not the what. And so, I think that a lot of the fundamentals of the business model between what you're talking about in the innovation-driven companies and the MNCs will probably still continue to play out. Just the how will change dramatically because we are getting differentiated results. I think another important part as we look forward is trying to figure out those new places that we can go into because a lot of biology and chemistry and even on the patient side is really unknown, and so seeing Lilly make the investment that they're making is actually a statement of, we know we want to try and get into that 90% or whatever% of the biology we don't understand today. We need some new ways to do it. Let's make an investment in there, and so I think we've taken a first step very early on that's progressed us towards that. I would expect other people to continue doing that. Thank you for the question. Just moving on now, beyond REC-4881 in the pipeline, looking at REC-1245, the RBM39 degrader, you're going to have early safety and PK data in 1H26. What do you think, what is possibly the size and scope of that data readout? And can we see that data in like a medical conference or a possible press release webcast like we did in December? Yeah, no, great questions. Yeah, look, this is a, the scope of it is a phase one monotherapy dose escalation expected to the size to be very consistent with what that is. What we're really looking for is RBM39, just as a reminder, you know, came from our phenomics platform. It's a novel target. You know, phenotypically it was similar to CDK12. That's been hard to drug because of the homology that you see with CDK13, whether it's transcriptional stress or DDR modulation are the areas that we're really focused on. So, step one, novel target, we want to be able to assess safety and tolerability. That's going to be really important. And by the way, this is a degrader molecule. Second thing, PK, we are going through various dose levels, so we also want to look at the exposure that we see. Yeah, in terms of next step, that's our first step. As we learn more, much more to progress further. Beyond these two programs, what would you have the street focus on for 2026? I know even 2027, you have some very interesting catalysts upcoming too. Yeah, for 2026, in addition to our wholly owned portfolio, also focusing on our partnerships. This is why I spent a little bit of time talking through it, whether it's with Sanofi, some of the milestones, we've had great momentum for milestones as of the last year or so, progressing those further to later stage milestones, potentially even to development candidate, which then triggers onboarding that asset. The other would be for Roche Genentech, you know, Maps, these AI Maps of biology are such a novel way of getting to that 60%, 70%, 90% of known, unknown biology. The next step is translating those into programs. That would be a first and really critical source of first-in-class programs that I think a lot of us are looking for. So, those are other milestones as well I would pay attention to. I'd also say, I think 2026 is the first year that you really see the business model play out in the way that it was meant to be. A lot of our foundational investment was to change this from being a binary risk model to something that's a more diversified, balanced business model. And so, now you see us with five programs in the clinic with over $500 million brought in from partnerships. We're actually able to make very data-driven decisions because we can look at it and say, there's not a single program that defines us as a company or our value. And so, we're able to be disciplined, and that'll certainly come through. I think that's a really important point. You know, we have a portfolio of programs for a reason. We have five programs, and we're going to be making just rapid go/no-go decisions so that we can double down on the areas we have most conviction in. You saw us do that six months ago with our portfolio prioritization. That's always something we'll continue doing. Any questions from the audience? All right. Your cash runway is anticipated to be sufficient through year end 2027. What specifically is assumed from pipeline development partnerships, which we've kind of touched on throughout our conversations? Yeah, so that fully funds everything that we've talked about in the milestones that Najat went through. What we have assumed is a probability weighting of milestones from our partnerships. So, we just looked at our existing partnerships and said, what are all the things that we know could move forward and apply probability weighting? But besides that, it's just our operations. All right. Final question for me, actually. So, now with the recent changes, you are now CEO, Najat. How are you thinking about the next chapter of Recursion? Yeah, I mean, very, very excited about the next chapter of Recursion. It really, you know, is the pillars that I talked through. We have a bold mission, and we are doing it in a very differentiated way. So, how do we really, number one, translate that into the proof points that, you know, is really going to continue setting us apart and drive towards novel medicines that matter? I mean, this is why I do what I do. Like the what, it's always going to be patients that have better medicines for them. The second is we have always been at the frontier of innovation when it comes to a platform, when it comes to AI, and being super surgical on which areas can we win in, can we partner in, and can we show the impact in our proof points. That's going to be another area because this is how you turn a company, you know, where even with pharma and some of the announcements that were made, is really that integration of tech and science, tech and medicine. That's how we bring that together. And then the third would be, look, I'm a big fan of ambition, but then also discipline. Discipline in our execution, discipline in our financial stewardship, and you've seen us do that already, and also discipline in operations. You know, that's where using agentic agents to just improve how we, not just the decisions we make, but how we operate as a company, you know, taking the waste out, taking the toil. And last but not least, look, people and culture have always been core. Without a great team, you're nothing. Here we have the pleasure of having a team that's super motivated, mission-driven, but also unique in their capabilities. I mean, this is what pharma will look like, right, in the next, hopefully, very soon. The combination of scientists that understand and appreciate and respect AI and have the rigor to use AI the right way. I want to underscore that enough. AI scientists who feel like they understand the complexity of science, drug discovery and development, which is incredibly humbling. It's an honor, and I'm humbled to be able to lead a company with such a mission and purpose. I am looking forward to seeing how the next chapter goes for you guys. Thank you so much for being here, and thank you, Najat and Ben, for being on the stage with me. Thank you. Thank you.