Recursion Pharmaceuticals, Inc. (RXRX) Q4 FY2025 Earnings Call Transcript & Summary

Good morning, everyone, and thank you so much for joining us. I want to start by briefly framing where Recursion is today and its journey and evolution. Over the past decade, Recursion has built something truly special - A differentiated platform, pioneering the integration of large-scale biological data generation, machine learning and compute to better understand the complexity of biology. We have also deliberately strengthened the foundation in chemistry, NAI through the acquisitions of Exscientia, Valence and Cyclica, creating a truly powerful foundation. Today, we're at an important inflection point. We're harnessing everything that we've built to date to do 2 things. Number one, translating insights into evidence; evidence that this platform, the use of AI end-to-end can generate medicines that matter. And we're doing this both across our wholly owned portfolio and through our partnerships with strong momentum across both fronts, and I'm excited to share some of the updates today. In parallel, we're also continuing to advance the platform itself. Today, we have what I like to call a trifecta, that's required to make impactful medicines. AI-driven biology, AI-enabled chemistry and AI applied to clinical development. We continue to invest to ensure we're defining the standard for how AI is applied across the full life cycle of R&D. And look, as we look across the sector, we are encouraged by the broader momentum in the field; new models, new players, new partnerships being announced, but the industry is clearly entering a new phase where value is being defined not only by the models you build and the collaborations that are announced but by actually translating those. This is the hard work into capabilities, into real application and measurable impact. The important question now is not only what you build, but what you can unlock. And that's the chapter Recursion is in. Our focus is on unlocking that value, using AI end-to-end consistently to generate better targets, better molecules and advance programs faster with repeatability. The ultimate goal is to deliver medicines that matter. So this quarter reflects that focus. We're making progress across all fronts. First, on the clinical side, our first positive proof of concept with FAP. On the partnership side, a fifth milestone with Sanofi, reflecting our growing joint portfolio, tackling highly challenging targets, we're excited to share more about that today and the continued evolution of our end-to-end AI platform. And last, but certainly never the least, disciplined execution, which is something we talked about at JPM, which has now extended our cash runway into early 2028. Look, there's a lot to cover today. So with that, let's jump right in. Today, we'll be making some forward-looking statements on this call, so please refer to our filings for more information. All right. We always at Recursion, start with the end in mind. And in that case for us, like I said before, it's medicines that matter that are truly differentiated. But in order to do that, you have to use the right data, models, compute and more. So look, there's a lot of talk about data. But what really matters is data that's high quality and fit for purpose. And at Recursion, our foundation has been building high-quality data at scale, not just 1 type of datasets, but multimodal across the board. This is where pioneering the lab-in-a-loop, pioneering the wet and dry lab has become incredibly important so that we not only generate data but then we generate purpose-built models that we test, learn and improve. The other thing I want to say is we sit in a sweet spot of being able to leverage both public data and our proprietary private data. That's incredibly important to ensure that our models are impactful, insightful and unique. And on top of that, I've mentioned this before, the importance of not just having the ingredients, but actually having a team who knows how to use it well, teams that are bilingual, fluent in science and in AI. But I want to add a third lens. It's also important to have reps under your belt to know what good looks like. And having talented teams that have reps is one of our core differentiators. But the ultimate secret sauce, I will say, is how it all comes together. Having an integrated end-to-end operating system that is a continuous learning loop all the way from novel biology or novel insights through to the clinic. Look, for many of us that have actually made medicines and are focused on this, which is a humbling effort, we all know that improving one decision in R&D is simply not enough. It's the compounded impact of better decisions across molecule, biological insight all the way through the clinic, that is what makes the difference. That's how you truly change not just the outcome, but also the time and cost of how you do things. And that's what we are focused on at Recursion. So what does that result in? First of all, in our clinical development, we have a diversified portfolio. We are very encouraged by our first AI-enabled clinical proof of concept with FAP, which has the potential to be a first-in-class for FAP, but we also have additional programs behind that. In addition to that, in our discovery portfolio, we also have another diversified set of programs. And specifically, I'll just touch on the partner piece where we have brought in over $0.5 billion in upfront and also milestones, and we'll share some additional updates today. I just want to say every single milestone we achieve is not just it improves the economics, but it's also a validation of the platform and a validation that we are learning fast in terms of what works, what doesn't to make our platform ever more intelligent. In addition to that, let's just talk a little bit about the platform. I'm going to share this slide every time we have an earnings because this is so core to what we do. Number one, being end-to-end, like I said before, is critical. You have to connect biology, chemistry to ultimately the patient, which is really where the rubber hits the road. That's where we are going. The other thing I also want to say is it's important to innovate not just on data generation, but also your models. So we have state-of-the-art, and I'll talk a little bit more about this foundation models, not just in phenomics, but transcriptomics and pulling those together in emerging virtual cell efforts that we're also focused on. We are also continuing to innovate on additional frontier models in the chemistry space as well as our newly built clinical development AI platform. Again, it is that integration and how you harness it to unlock value that matters the most. Next slide. So in terms of our strategic pillars, we have 3 main areas that we're doubling down on in this new chapter. Number one, tangible proof points. This is so important, both from our clinical portfolio as well as our partner programs. Second, like I said before, in parallel, continuing to invest surgically in our platform grounded in areas that will enable us to have more of those proof points. And third, but certainly not the least, pairing that bold ambition that we have with disciplined execution, how do we do more with less. So let's go through each of these. If you go to the next slide, one area that's really important for us is we like to track what are our wins and learnings as we go through each of these pillars. So you'll get used to seeing that as well going forward. First, in our first pillar, which is really focused around making progress around clinical pipeline as well as our partner programs. First, FAP. This is really, really important data for a disease that has no approved therapies to date, durable and meaningful polyp burden reduction. Second, today, we'll highlight our Sanofi collaboration. Just as a reminder, this is where we're tackling challenging targets in I&I and oncology and leveraging our AI component -- chemistry component of our platform to design novel compounds. And here, we just achieved our fifth milestone to date. We'll do a double-click on this, but this is an example of the repeatability of our platforms, especially around using AI to develop chemistry molecules and small molecules. Second pillar is really focused on our platform. And I want to highlight 2 things here. As we look across the portfolio, we look at green shoots, as I like to call it, proof points where we're actually seeing that we can do things better and faster. So one example is, again, in our AI-enabled chemistry platform. When we look across the portfolio, we're synthesizing 90% fewer compounds than what we see in the industry. So about 300 versus 2,500 compound synthesis. This is because we are predicting more and making less. This is where in silico approaches should be guiding us, and we're seeing that happen. And we're doing this 2x faster. So instead of it taking us -- taking the industry 42 months, we see on average, it takes us 17 months. We're going to keep pushing on this. The other area, let's talk about biology. We talk constantly about the amount of unknown biology. And what we're trying to do is generate and we have generated first in industry maps of biology, these huge Atlases where we are trying to uncover unknown biology. This is in partnership with our great partners at Roche, Genentech, 2 back-to-back maps that were just accepted. And now the team is hard at work in translating those maps into novel biological programs. And our third pillar, momentum with discipline. Look, we have a lot of things we want to do, but we have to do it with discipline and good financial stewardship, financially, of course, but also operationally. And we're really excited to share that, first of all, we've seen a 35% reduction in pro forma operating expenses year-over-year. This has come from multiple areas, sharper focus on our portfolio, yes, but then also optimizing our G&A and improving our platform efficiency, which an example of it you just heard about in the last slide in terms of the number of compounds we're synthesizing, our speed, et cetera. And the other thing that we're excited to share today is extending our runway to early 2028. All right. So let's dive into each of these pillars a little bit more. Starting with our wholly owned pipeline. Look, when we look at the number of programs here, we have a diversified portfolio. There are different types of differentiation across each of these programs, and I'm going to categorize it in 3 ways; number one, there are programs with novel biological insight from our platform. Number two, there are programs that have emerging biology, interesting biology which is unconquered, not validated yet, and we have developed optimized programs. And then the third is really focused around areas that have validated biology with the significant unmet need that still exists from a patient perspective. So you've seen this slide before. We always track which components of our platform are we using across our various programs. So let's dive into a little bit more around the 3 categories, starting with the platform-derived novel biological insight. All right. 2 programs that exist in that category. one, FAP REC-4881. First of all, I don't need to say again, but like the reason why there's such a significant unmet need. There is nothing approved for these patients. This is a disease that is hallmarked by hundreds of polyps, each and every one of which is precancerous and has 100% risk of CRC, colorectal cancer by the time of 40. More than 50,000 addressable patients in the U.S. and EU. The Recursion differentiation is using the Phenomics, the early version of the Phenomics platform to ascertain in an unbiased fashion that MEK1/2 inhibition could actually work in FAP. We have just completed our Phase II study. We had a positive clinical POC, which we just shared in December. And I'll share a little bit more about the data, just to recap for those who might have missed it. And one of our core next steps, and we're on track is to initiate FDA engagement on the registrational path first half of 2026. We also have another program that has similar elements from a differentiation perspective, RBM39. RBM39 look is going to be potentially important in genomically unstable cancers. And from a patient population, as you can see that, that impacts a wide patient population. The differentiation for Recursion and our platform really came from uncovering this MOA and the connection it has to CDK12, which is known to be important for DDR modulation for many decades, but challenging to target because of the similar homology with CDK13. Right now, that program is in Phase I monotherapy dose escalation, and we expect to share an early Phase I update on safety and PK first half of 2026, so later half of this year. All right. Let's go to the next category, emerging biology, that unconquered biology and where we can optimize program. There, we have CDK7 and ENPP1. And you'll see what we're doing from an optimizing the program perspective is both on the chemistry side and also on the clinical development side. So let's start with CDK7. CDK7 has been known for a long time to be an important central master regulator, both of cell cycle control, but then also of transcription, with a wide variety of patient populations that are addressable, given its centrality in oncology. From a Recursion differentiation perspective, others have tried this target before. And one of the key challenges has been optimizing the PK/PD, optimizing the therapeutic index. That's where we have leveraged the second element of our platform, AI chemistry in order to optimize the molecule, especially around gut permeability. We also are leveraging our platform in order to figure out which patient population should we go into that could potentially impact the most from CDK7 inhibition. Progress right now, we finished our Phase I monotherapy dose escalation, maximum dose has been selected, and we are in progress of the combination study, which is focused on ovarian cancer, second-line platinum-resistant with more data expected first half of 2027. And again, apologies, we're working very hard at Recursion, which is why I have lost my voice, but I will try to make it through the rest of this presentation. All right. The next program that's also in this category is focused on ENPP1. ENPP1, loss of a certain mutation leads to challenges with bone mineralization, thereby leading challenges in fractures, pain, et cetera. Again, another lifelong disease that starts very early in the patient's trajectory -- life trajectory. The Recursion differentiation here is focusing on a molecule that can actually be oral because what's available today for patients and also some of the efforts in investigational agents is around enzyme replacement therapy. That requires a huge patient burden in terms of injection, subcutaneous, sometimes multiple a week. So what we wanted to do is design a molecule for ENPP1, which again, is challenging target, especially in this space for hyperphosphatasia which can be suitable for chronic dosing. IND-enabling studies ongoing for this program right now, and we expect to have a go/no-go decision second half of this year on this program. All right. The third category. Look, these are some of the -- some targets that have validated biology but have significant unmet need that exists. So let's take MALT1. MALT1 is validated from a target perspective in B-cell drivers. But some of the challenges really have been around limitations around tolerability. So we, again, leverage our Recursion platform to really design molecules that could design away from some of the UGT1A1 and other off targets that have been seen, which are going to become increasingly important with combination with BTK inhibitors and others, which is what will be the ultimate efforts in this space. So we have Phase I monotherapy dose escalation ongoing with early Phase I update data again on safety and PK, monotherapy expecting first half of '27. Another program that is a similar theme is LSD1. LSD1 is known to be an epigenetic regulator, really trying to prevent or inhibit some of the differentiation that you see in solid tumors such as small cell lung cancer and also AML with some validated data seen in AML recently. And the differentiation, again, here is, can we design out some of the challenges around tolerability, which has led to some DLTs and not being able to dose up high enough, such as thrombocytopenia. This too, Phase I monotherapy dose escalation is in startup and next steps is to have early Phase I update on safety and PK monotherapy expected second half of 2027. Again, we expect to start to understand if some of the tolerability improvements we're trying to do, can we actually see that early on. This is our theme around early go/no-go decisions to really understand is the design playing out in the clinic. And another program that's in preclinical and late preclinical is our PI3K1047 mutant selective. PI3K in general is an important oncogenic mutation linked to resistance and relapse, et cetera. And I'll walk through a deep dive in terms of some of the latest data we have here. where again, remember, we use our platform to design a molecule that would be much, much more selective over 100x of selectivity over wild-type PI3K, which leads to some of the tolerability challenges that leads to dose interruptions and reductions and more to come there, but that's an IND-enabling study. Again, go/no-go decision second half of this year expected before we consider a Phase I initiation. So I know that was a round trip around our portfolio, but I would love to actually double-click on one of our later stage, which is REC-4881 and then also one of our earlier stage and potentially entering our clinical pipeline, which is our PI3K program. So let's go through the REC-4881. I'm just going to do a quick update here. For this program, we had our clinical POC late last year. And a couple of things to note. No approved therapies. What we saw in our Phase II, 3 months on treatment with 4-milligram QD of this MEK1/2 inhibitor, significant polyp burden reduction, by 43% median. Highest -- one of the higher polyp burden reductions to date, 75% of the patients responded. In terms of the AEs that we see, very much in line with what you see for MEK1/2 inhibitors, majority were grade 1, 2, rash, CPK and no grade 4, 5 to date. What we also saw, which was even more encouraging was when these patients were then off treatment for 3 months. And remember, this is a chronic disease. So the on/off element is going to be really important for us to understand. And we're the first to actually look at on and off in this disease area, we see continued durable polyp burden reduction, in some cases, actually deepening and with a significant amount of the patients actually responding. So this is a really important -- when I said at the top of the call, like it's important to not just have insights, but how do you turn those into something that's meaningful for patients and then ultimately new medicines. So I won't recap in terms of the insight to proof point, but I'll focus on what's next. We are on track, as we discussed late last year in terms of the FDA engagement, initiating that first half of 2026 to really discuss the registrational study design. In addition to that, we have already started the enrollment of 18 and over cohort. As you remember, some of the data we shared was for 55 and over. So we're already progressing on the 18 and over and then also advancing dose optimization efforts, really inspired by what we saw with the durability data that I shared on the last slide. So we expect to have additional clinical data first half of 2027 as well. So stay tuned, more to come. Now let's move to another exciting program that we have in our pipeline. This is our PI3K 1047 mutant selective. So look, for PI3K, I'm sure you're thinking there are multiple PI3K. Why are we working on PI3K? First of all, this is a very, very important target across multiple solid tumors. The current PI3K inhibitors have been constrained, and we have some data that we'll share shortly, hyperglycemia, metabolic toxicity, dose interruptions, dose reductions, limited treatment duration. All of that means, is there an opportunity to do better by patients. There's an unmet need that still exists. So what is our differentiation and what is our thesis is really focusing on the 1047 mutant selective, which has 100x more selectivity over wild type, thereby having the potential to minimize risk for AEs. And in order to do that, we designed a molecule that can allow us to have that exquisite selectivity. And with that, let me just actually walk you through something that's very exciting from a platform perspective. For this program, we started off with X-ray and structures where we had proprietary structural insight. And that led us to leveraging our ND simulations, and this is where compute becomes really important. Our molecular dynamic simulations revealed a novel pocket. We then used our generative 3D modeling efforts and machine learning in order to design molecules, novel scaffolds for this novel pocket. And we were able to use other approaches, our other ML approaches to really rapidly design our cycles so you get exquisite potency, but then also selectivity. Remember, it is that selectivity that leads to the tolerability challenges we talked about. And I want to take a moment, like if just look at the lower bar here, in order to design this compound, we designed 242 compounds, 13 cycles in 10 months. This is what we want to see from a green shoot perspective of the platform. Can you do it better? Can you do it faster? And this is what we're tracking across our entire portfolio. I can tell you, compared to industry standards, this is fast. And this is what gets us excited, data that we can actually do things better, faster. But then the next question is, how does this molecule do? So I'll share some preclinical data that we haven't shared before. First, let's look at how it does from a tumor reduction/regression perspective. So if you look at the left-hand side over here, what you're looking at here is a dose-dependent tumor regression for our compound, which is in blue. And we actually also looked at some of the compounds that are in the market, such as PIQRAY and also Scorpion's compound, just to get a sense of how we're doing. And we see significant tumor regression, not just reduction but regression with this compound, comparable to what you see with Scorpion and much better than what you see with PIQRAY. But given the standard of care, we also wanted to see the performance versus standard of care. So with SERDs with CDK4/6 inhibitors, which is that come as the standard of care today. And what's exciting to see here is the synergy. Monotherapy, yes, you see reduction and regression with our compound, but you actually see synergistic efforts with the standard of care. This is very encouraging. We actually have additional data. We only have so many charts we have space for, where we also looked at other encouraging assets in the space such as capi. And we saw improved tumor regression with low dose of our asset versus high-dose capi. So all in all, this is encouraging from an efficacy perspective for this compound. But then we also wanted to look at tolerability. So here, what you're seeing is animal models from both naive wild-type and then also obese diabetic animal models as well. On the left-hand side, you see we don't see any impact on hyperglycemia markers in naive wild-type mice versus what you see with Scorpion and PIQRAY as well, which is encouraging. This is what we are designing the molecule to do. And then if you go to the right side, a little bit complicated, but we like to share data also in obese diabetic rats, you don't see hyperglycemia or the metabolic liability even at supra-efficacious dose for our asset versus Scorpion and PIQRAY as well. So again, taken together, this is encouraging. But like I always say, the rubber hits the road in the clinic. So what does this mean from a clinic perspective? Look, current PI3K inhibitors focusing on HR-positive breast cancer, they do have tolerability limitations. 65% to 85% experienced hyperglycemia, large percent actually also have dose interruptions, dose reductions, some of it is driven by the hyperglycemia they're experiencing. And we also did some real-world analysis as well given our clinical development AI platform, but really thinking about what the target product profile could look like. And you see the discontinuation about 3 to 6 months. And that's not a very long time. So I think the potential here, and we'll have to see, a, how the compound does through IND-enabling studies, and that's where we are today. is can we expand that patient population in twofold. Number one, in breast cancer, not just in patients that are nondiabetic, but also patients that are prediabetic and diabetic if this trajectory of hyperglycemia markers and not having impact holds. That's about 50%-50% in breast cancer. And then the other is there's also a broader patient populations such as colorectal and endometrial we can also explore. And one thing I'd be interested to also look at is can these patients because of the better tolerability, stay on longer, longer treatment duration to really maximize the impact of these therapies. But again, clinical validation and improved tolerability requires is critical to confirm this expansion thesis. So if you go to the next slide, more to come. But again, we keep looking at these args. What was the insight? What do we design the molecule? What are the early proof points so far that you saw with preclinical data? And what's next right now is the go/no-go decision for Phase I, which would be second half of this year. So currently, the study is an IND. All right. That was just our first pillar, double click. We'll also do a little bit more around our partnerships. I'm really excited to share the progress we are making because remember, proof points can come from both your internal portfolio, which is what we just focused on, but then also from our amazing partners that we're working with on actual programs. To date, we have already achieved over $500 million in total cash inflows from our partnerships, both upfronts and milestones. And we've actually laid out some of those recent ones with the momentum that we've been achieving recently. But I want to emphasize something that sometimes gets lost. Each and every one of the programs that we're working on has the potential for over $300 million in milestones and tiered royalty per small molecule program. Some of the royalties are up to double-digit royalties. So this is significant economics and also validation opportunity for Recursion. All right. We're very, very excited for the first time today to unveil our joint portfolio with Sanofi. I mean Sanofi has been a fabulous, fabulous partner. We learned so much from that exceptional team, both across I&I and oncology. And what we're showing here is the multiple programs that we're working on 5 and with multiple early discovery programs as well. And you see just like our internal pipeline, this is also a diversified pipeline. It's focused on challenging targets in I&I and oncology with molecules that have the potential to be first-in-class and/or best-in-class, with programs that address very specific unmet needs. So thinking with the clinic and then mind. And to date, we have advanced 5 lead packages that has been delivered by Recursion across 5 of these programs and accepted by Sanofi to date. That's about $34 million in milestones to date in addition to the $100 million in upfront, so $134 million so far. And I just want to say we have a lot of important work ahead of us with later-stage discovery milestones over the next 18 months. And look, discovery is probabilistic. We know some will work and some of these programs won't, but it is the repeatability and the ability for our platform to have multiple shots on goal. That's incredibly critical for us. That's what you see with our internal portfolio. That's what you see as we work humbly with our partners to also advance important programs for patients in areas that are challenging. So just double-clicking on one of these, how do we get there? Remember, these are challenging targets, and we are leveraging our platform. And I just want to explain one aspect that I think is really important. Our platform is not about one data, one model, one asset. It's about the confluence of a suite of them that you use for the problem at hand. So we start with the problem first and then you have flexibility and optionality across our models to get to the best outcome. And so again, our latest program where we just got a milestone, our fifth milestone that we just achieved in the oncology program really focused on leveraging, a, these are targets that are data poor. So we leverage both our physics-based approaches as well as our machine learning approaches, physics-based to really understand the protein flexibility better, find novel target, novel pockets and then leverage our machine learning algorithms in order to rapidly do our design test cycle and find highly potent molecules that are now progressing to the next stage. Very exciting progress here. And stay tuned, more to come. But this is truly what proof points look like, actually showing value that will matter for the medicines that we are working towards. But look, none of this can happen without a unique and differentiated platform that is an ever important work in progress. So I want to just do a snapshot of the 3 components of our platform, starting with biology to insight. I mentioned about the proprietary data that Recursion has been building for a decade, over 50 petabytes of high-quality multimodal data, and I want to emphasize the multimodal piece. Biology is complex and having diversity of data and having at-scale data sets complete to the extent possible, whole genome knockout, overexpression. That's the kind of model -- data that you need to then build foundation models that are state-of-the-art. We have a fantastic team that's working on this, whether it's in the phenomics foundation models or the transcriptomic foundation models and combining those is the fusion of those models that are going to be really, really important in biology because we all know we need to connect input to output, genetics, transcriptomic, proteomic, phenomic patient data, that's the effort that we're focused on. And how do we leverage it because that's the so what is what matters, is creating these novel proprietary data sets. We call them biology maps, and we have those internally across different therapeutic areas. We also have it in neuroscience and GI, Onc with Roche, Genentech. And that's what those insights is what's fueling our discovery pipeline. The next area is focused on leveraging AI for chemistry, novel small molecules. I can tell you, this is harder than it looks. And we have used our In Silico approaches to generate over 100 million molecules. One emphasis -- one point I want to emphasize is the point around synthetically aware design. It's one thing to design molecules that are interesting, but if you cannot make them, then that limits or if you can make them, but the CMC is very challenging, that really limits that end in mind. We always start with that end in mind, the target product profile, what can be a true drug that matters? We do that across our partnerships and our internal portfolio. And like I said before, 90% of these molecules are generated, score, prioritized by our models. And one thing that we're doing increasingly, not just leveraging automation, but also agentic orchestration so we can get things done better, faster and in a more unbiased approach. And I mentioned the stat before, but I can't wait to mention it again. Look, we, on average, across the portfolio. So with PI3K, you said 242 compounds, 10 months. But across the portfolio, we like to be transparent around our data. 330 compounds is what we synthesize on average versus 2,500, 5,000 in industry. And we do it in 17 months on average versus 40 months plus for industry. This is -- these are the kinds of things that we track. And that's going from target all the way to advanced candidate. And as a result, we have over 10 development candidates across our internal portfolio and getting to that line with our internal and partner programs as well. And last but certainly not the least, is an area that I get a lot of questions about as well in terms of our newly built emerging clinical development AI platform. What we have done first, and again, just like we did with our biology platform and chemistry, you got to build a really good data foundation, 300 million-plus real-world lives that's through both some internal work, but then also the great ecosystem, integrated data partnerships. We're very opportunistic around that. So some of the early results, I mean, you can read the bullets here for the one that I would point the attention to is enrollment rates. Look, we are -- in order to execute on programs, you have to enroll in a very efficient and intelligent way. And some of our early results for some of the programs, we're starting to see 1.3 to 1.6x improvement. We are also just improving the operational piece that goes underneath it in terms of just starting studies faster by up to 3 months. All of this accumulates. Remember the point around the compounding impact of decisions across the platform. This is how you do. This is how you define drug discovery and development, leveraging AI. And let me just give you a sneak peek as to how that works on the enrollment front. So we start with the 300 million patient lives. Our platform can actually generate a heat map, just like you see for biology or chemistry in different ways. But here for potential patients across -- and we're showing the U.S. here, across the country. Then we can go into deeper resolution at a state level and then at a ZIP code -- 3-digit ZIP code level and then at a site level. And what's really important here is we can also get data around the site's experience. with running that trial. And this is -- you can probably guess from which program, ovarian cancer trials and how many competing trials that exist, that becomes really important. You don't want to fish in the same pond; that can lead to delays. And then beyond that, we can also get what -- how many patients do these sites have. And then you can do a filter in terms of your inclusion exclusion and what's relevant for the type of patient that we are looking for in a specific study. That filter does not get happen enough, I can tell you in traditional approaches. I call this, we talk about precision medicine, precision biology, precision chemistry. This is precision operations and starting with the patient in mind. With that, thank you for being with me for some time. I want to now hand it over to Ben Taylor, our CFO, to actually go through some of our financials.

Thanks Najat. So 2025 was a year of financial transformation for the company. As a part of the integration, we decided to rebuild all of our corporate systems from the ground up. This was really important because we wanted to be able to apply the same level of discipline and rigor to our strategic decision-making that we do to all of our scientific decision-making. And so we looked at how every dollar in the company goes towards a specific quantifiable outcome. And that's how we were able to achieve the efficiencies that we did over the last year while still advancing a portfolio of 5 clinical programs, hitting multiple different partner milestones, really investing behind the growth in our platform as well. And all of that comes back to focus on those investments across our pipeline and technology portfolio that have the best risk return, that are going to give us the most impact for the investment that we're making. And so that's how we were able to come back and have a 35% year-over-year reduction from pro forma '24 to '25 and even come in 10% below the guidance that we originally provided in May of last year. So we ended the year with $754 million in cash. Looking forward, our 2026 cash operating expenses are expected to be under $390 million. Cash operating expenses is a non-GAAP measure that we're going to be using to give you guidance. We have a lot of noncash expenses in our P&L. And so we wanted to provide something that showed what our cash profile might look like going forward. And so this is coming directly off of our cash flow statement. If you look at operational cash flow and then you add back our inflows from partnership and transaction costs, you'll be able to get directly to this guidance number that we're using. In addition, last year, it was really exciting to see that we crossed the $500 million milestone in cumulative partner inflows. We expect to continue to achieve those going forward. And in fact, we hit our first milestone earlier this month already. And so we do include a probability weighting of some of those milestones in our cash flow projections going forward. That's actually the really exciting part for me is not only were we able to exceed our efficiency expectations, but that actually means we need to extend out our cash runway. And so we're updating our guidance to go to early 2028 as of now. And with that, I will hand it back over to Najat.

Thank you so much, Ben. We'll wrap it up by just saying looking ahead, we have a very broad set of catalysts that are coming up, and it's going to be a busy next 18 to 24 months. We'll see if I can recover my voice soon. In terms of this year, like I said, we're on track for our initial engagement with the FDA on REC-4881. We're looking forward to that and also initial data, early safety and then also PK for RBM39 and go/no-go decisions for PI3K and ENPP1, which are both in IND-enabling. We'll also have additional data for 4881 early next year and then combo data expected for our CDK7 program as well as more early safety and PK data from MALT1 and LSD1. Recall, for both of those, we designed the assets to be more tolerable. So these are going to be important. And I know the partner catalyst looks like a small box here, but I wish I could physically expand it because that's going to be very important. Our partnerships with Sanofi, as we just discussed in terms of multiple programs as we're progressing into more later-stage development candidate and other milestones as well. But in addition to that, these maps, these maps where novel biologies really would come from extracting that into new programs with Roche, Genentech, et cetera. So really, really important work that continues. And we continue to invest and push the boundaries in terms of our platform, defining what industry and standard really looks like for making medicines using AI. And as Ben just mentioned, pairing all of that important work with disciplined execution. We've really pivoted towards an outcomes-based budget where we test what every dollar, what value creation every dollar can drive. So doing more with less. So I'll close by saying thank you so much for the time. And also, our focus will always remain on value creation for patients. They're the ones that we ultimately serve. Patients are waiting and also for, of course, our shareholders. So thank you again for listening. And with that, I'm going to pivot to the Q&A section. And I'll also have our CSO, Dave Hallett, joining us as well in addition to Ben Taylor in order to address some of the questions.

All right. From Sean at Morgan Stanley and Priyanka as well, thank you for the questions from JPMorgan and from Brendan at Cowen. So many people. Questions around REC-4881, understanding what potential registrational pathway may look like upon alignment with the FDA, how we're thinking about providing a regulatory update and updated patient population? It's a long question. I'll break it into pieces. In terms of the regulatory update, as I mentioned, for us, we're on track for that engagement, initial engagement with the FDA first half of 2026. All hands on deck for that. That's going to be really important in terms of discussing their potential design for registrational study, patient population, endpoints. We have a very compelling data set in terms of the durability and then also polyp burden reduction. In addition to that, I didn't cover it today in the interest of time, we also have the natural history data as well. So coupled with that, it's going to be really important for us to have conversations with the FDA. So that's point one. Point two is also around the updated patient population. So as I mentioned, 18 and over, that arm is already recruiting as well as we're also looking at dose optimization schedules just given what we saw with our durability data. So more data on that coming first half of 2027. Look, as we have meaningful updates across both fronts, as you have seen, we've done webinars, ad hoc. We like to be real time and transparent when we have more meaningful outcomes and updates, we'll absolutely be sharing with the Street as well. All right. Next question is from Alec from Bank of America. It looks like the cost-cutting measures -- cost optimization, cost-cutting measures really started to take hold in Q4. Any one-offs that helped in the quarter? Or are these levels sort of the expectation for the go forward? Ben, do you want to take that?

Sure, happy to. Yes. Thanks, Alec. So if you think about it, I agree with that, it's really about efficiency more than cost cutting. So we have hit a point where we have gone through all of the integration. I would assume that, that is all complete. There's no big one-offs in the system. But what we are really trying to do is come in with attitude where we want to continue to find ways for every dollar to make more of an impact in the following years and months than it did previously. And so when you come in with that attitude, all of a sudden, you start to find ways to do more with less. And that's where we expect to be able to continue growing our pipeline, investing heavily behind our platform and moving things forward while still hitting those cost targets that we put out there.

Great. Thank you, Ben. I mean the only thing I'll add is, Alec, I think it's piece around rapid go/no-go decisions and how we are doing that, just the mentality and the mindset and also understanding and just taking a step back, the variety of areas we're working on and what is the value proposition across the different areas, which has evolved as you generate more data, almost thinking like an investor, I think, is really important, being agile around capital allocation, and that's what we will continue to do, of course, being driven by data. Great. Next question. NVIDIA. What's the rationale in terms of the divestment? Do you plan to seek other technology partners? Does NVIDIA now have proprietary insights from the models you've trained, et cetera, et cetera? Okay. I think it's going to be this great question. Thank you so much. It's going to be important to decouple 2 parts. One is the investment from NVIDIA and one is our collaboration, our technical collaboration with NVIDIA. The technical collaboration with NVIDIA continues. I mean some of you might have just seen, we're going to be highlighted in a lightning round for NVIDIA's upcoming GTC presentation with HighRes, really being the -- Recursion being a pioneer in how to leverage automation, this wet and dry lab. This is not just words. This is actually in action. This is how we do millions of experiments a week. The other piece is also our collaboration with NVIDIA around our BioHive-2, one of the fastest supercomputer in life sciences. That -- I mentioned the examples around PI3K around Sanofi, using machine learning, using molecular dynamics. All of that is underpinned by our supercomputer. Our partnership with NVIDIA couldn't be any stronger. So that continues. In terms of the divestment, this really was, if you look at the public 13F filings from Q4 of 2025 is really a shift in NVIDIA's investment portfolio to more larger on-strategy supercomputer data center, et cetera, efforts. And so that's really an investment portfolio shift, and we were not the only company, there were other decisions made as well. It's a collective shift from a portfolio to more on-strategy investment, large, large $1 billion plus investments. So those are 2 areas to be decoupled. The last thing I'll also say you also -- sorry, there's so many questions, also asked a question, are we seeking other technology partners? We have a strategic partnership with Google as well in terms of cloud compute. We have the partnership, as I mentioned, with NVIDIA on machine learning and models, et cetera, but also on-prem compute. And we will continue. We are -- we've always been one of the pioneers in really bridging the world of tech and science, and we'll continue to do that. All right. We'll take one more question here. From George is with the recent positive preliminary efficacy data for REC-4881 in FAP and the achievement of your fifth milestone with Sanofi, what specific metrics or historical comparison from your current clinical portfolio best demonstrate that Recursion is improving the probability of clinical success or speed of development compared to traditional discovery methods? I'm going to hand it over to Dave Hallett to get us started, and I'm sure we can also add some more comments as well there.

Thank you, Najat, and good morning and good afternoon to those of us in Europe. I think I'll maybe start from the discovery perspective. I think Najat's during the last presentation has highlighted a number of themes. One is about the repeatability of kind of delivery. I think that specifically highlighted in the burgeoning Sanofi kind of pipeline that we're kind of -- that we're building together. This is kind of a repeatable platform that's kind of delivering both best-in-class and kind of first-in-class challenging targets. Above JPMorgan, again, in this presentation, I think we've highlighted the speed of delivery. If you look at the metrics that we're delivering in terms of numbers of novel compounds that we synthesize and test and the speed that we're getting to these development candidates. These are, I think, further demonstration that the role kind of technology plays in kind of accelerating that delivery. The proof is ultimately in the clinic. And clearly, we're very excited for patients in terms of FAP. I think this is the first example from our platform where we've been able to kind of demonstrate that a compound that came from Recursion has shown clinical proof of concept. And obviously, the goal over the coming months and years is to show repeatability in that frame as well.

Thank you, Dave. And just to maybe add a little bit of a broader perspective, looking at Recursion, 5-plus clinical programs, a diversified portfolio on the clinic side, a diversified portfolio on the discovery side. And in the time and effort it takes to build a platform. I mean, these data sets didn't exist, the models didn't exist. All of that I just think taking a big step back, we are not a 1, 2 asset biotech. And we are a tech bio for a reason, which is the piece that Dave just mentioned really well, which is what we're really trying to focus on is the repeatability, the scalability, making all of this much more engineering focus using whether it's agentic agents or automation to do things better, faster, taking toil out of the system so we can supercharge our scientists more and more to do the hard work. I just want to emphasize the hard work of drug discovery and development. Drug discovery and development inherently is probabilistic. Most things don't work. We have a 90% failure rate. So we know that multiple shots on goals is going to be important. So that's the kind of fortitude and resilience that's needed in this space, and we're adding an area to worlds coming together in tech and bio that haven't really come together before and not just building models that are interesting but actually apply models that unlock value. And so just to tie it together, we are constantly looking at metrics and stats. The team knows, I call it green shoots, whether it is the number of compounds we synthesize just 90% less than the industry, the speed with which the cost of our INDs, we do the same thing in the biology platform. We do the same thing with the clinical development, as you saw me share, where we're seeing improvement in enrollment and so forth. We are -- there's so much work to be done, but this is what, quite frankly, gets us excited. It is hard, but incredibly challenging and rewarding work. So thank you all for your support to our partners, to our shareholders, but, most importantly, to patients that are willing to take a bet on us in our programs and that are waiting, and we are working as hard as possible to really forge a new era of how medicines are made for patients that are waiting. Thank you again for joining us today, and we look forward to sharing more updates in the coming months.