Q4 2025 Recursion Pharmaceuticals Inc Earnings Call

February 25, 2026

Speaker #1: 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.

Speaker #1: We have also deliberately strengthened the foundation in chemistry and AI through the acquisitions of Exscientia, Valens, and Cyclica, creating a truly powerful foundation. Today, we're at an important inflection point.

Speaker #1: We're harnessing everything that we've built to date, to do two things: number one, translating insights into evidence, evidence that this platform, the use of AI end-to-end, can generate medicines that matter.

Speaker #1: And we're doing this both across our wholly owned portfolio and through our partnerships. With strong momentum across both fronts, I'm excited to share some of the updates today.

Speaker #1: 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.

Speaker #1: In parallel, we're also continuing to advance the platform itself. You know, today we have what I like to call a trifecta that's required to make impactful medicines.

Speaker #1: 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.

Speaker #1: 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 lifecycle of R&D.

Speaker #1: And look, as we look across the sector, we are encouraged by the broader momentum in the field. New models, new players. Partnerships being announced.

Speaker #1: But the industry is clearly entering a new phase. We're values being defined not only by the models we build and the collaborations that are announced, but by actually translating those.

Speaker #1: We're harnessing everything that we've built to date to do two things: Number one, translating insights into evidence—evidence that this platform, the use of AI end-to-end, can generate medicines that matter.

Speaker #1: 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.

Speaker #1: And we're doing this both across our wholly owned portfolio and through our partnerships. With strong momentum across both fronts, I'm excited to share some of the updates today.

Speaker #1: 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 advanced programs faster with repeatability.

Speaker #1: 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.

Speaker #1: And 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 with our first positive proof of concept with FAP.

Speaker #1: 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.

Speaker #1: And look, as we look across the sector, we are encouraged by the broader momentum in the field. New models, new flares, partnerships being announced, but the industry is clearly entering a new phase.

Speaker #1: 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.

Speaker #1: We're 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.

Speaker #1: Which is 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.

Speaker #1: 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.

Speaker #1: Using AI end-to-end consistently to generate better targets, better molecules, and advanced programs faster with repeatability. And the ultimate goal is to deliver medicines that matter.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: So this quarter reflects that focus. We're making progress across all fronts. First, on the clinical side with our first positive proof of concept with FAP.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: Not just one type of dataset, 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.

Speaker #1: 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 is now extended our cash runway into early 2028.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: That's incredibly important to ensure that our models are impactful, insightful, and unique. And on top of that, you know, 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.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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 one type of data set, but multimodal across the board.

Speaker #1: 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.

Speaker #1: 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 and learn and improve.

Speaker #1: Look, for many of us that are actually made medicines and have focused on this, which is a humbling effort, we all know that improving one decision in R&D is simply not enough.

Speaker #1: 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.

Speaker #1: 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 and how you do things.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: We are very encouraged by our first AI-enabled clinical proof of concept with FAP. Which is a potential to be a first-in-class for FAP. But we also have additional programs behind that.

Speaker #1: 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.

Speaker #1: In addition to that, in our discovery portfolio, we also have another diversified set of programs. And specifically, I'll just touch on the partnered piece where we have brought in over half a billion in upfront and also milestones.

Speaker #1: 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.

Speaker #1: And we'll share some additional updates today. I just want to say every single milestone we achieve is not just, you know, it's improves the economics, but it's also a validation of the platform.

Speaker #1: 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.

Speaker #1: You know, I'm going to share the 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.

Speaker #1: 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.

Speaker #1: You have to connect biology to chemistry to ultimately the patient, which is really where the rubber hits the road. That's where we are going.

Speaker #1: We are very encouraged by our first AI-enabled clinical proof of concept with FAP, which is a potential to be a first-in-class for FAP. But we also have additional programs behind that.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: I just want to say every single milestone we achieve is not just it's improved 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.

Speaker #1: Next slide. So in terms of our strategic pillars, we have three main areas that we're doubling down on in this new chapter. Number one, tangible proof points.

Speaker #1: In addition to that, let's just talk a little bit about the platform. I'm going to share the slide every time we have an earnings because this is so core to what we do.

Speaker #1: 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.

Speaker #1: Number one, being end-to-end, like I said before, is critical. You have to connect biology to chemistry to ultimately the patient, which is really where the rubber hits the road.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: You know, 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.

Speaker #1: 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.

Speaker #1: First, FAP. You know, this is really, really important data. For a disease that has no approved therapies to date, durable and meaningful poly burden reduction.

Speaker #1: Next slide. So in terms of our strategic pillars, we have three main areas that we're doubling down on in this new chapter. Number one, tangible proof points.

Speaker #1: Second, today we'll highlight our Sanofi collaboration. Just as a reminder, this is where we're tackling challenging targets in INI and oncology, and leveraging our AI component chemistry component of our platform to design novel compounds.

Speaker #1: 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.

Speaker #1: Second pillar is really focused on our platform. And I want to highlight two 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.

Speaker #1: 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.

Speaker #1: So one example is, again, in our AI-enabled chemistry platform. When we look across the portfolio, we're synthesizing 90% pure compounds. Then what we see in industry.

Speaker #1: First, FAP. This is really, really important data. therapies to date, durable and meaningful poly-burden reduction. Second, today we'll highlight our Sanofi collaboration, just as a reminder this is where we're tackling challenging targets in INI and oncology and leveraging our AI component chemistry component of our platform to design novel compounds.

Speaker #1: So about 300 versus 2,500 compounds synthesized. This is because we are predicting more and making less. This is where in silico approaches should be guiding us.

Speaker #1: And we're seeing that happen. And we're doing this two times faster. So instead of it taking us taking, you know, the industry 42 months, we're seeing an average it takes us 17 months.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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 Genetics, two back-to-back maps that were just accepted.

Speaker #1: The second pillar is really focused on our platform, and I want to highlight two 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.

Speaker #1: 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.

Speaker #1: So one example is, again, in our AI-enabled chemistry platform. When we look across the portfolio, we're synthesizing 90% pure compounds. Then, what we see in industry—so about 300 versus 2,500 compounds synthesized.

Speaker #1: Financially, of course, but also operationally. And we're really excited to share that, first of all, you know, we've seen a 35% reduction in pro forma operating expenses year over year.

Speaker #1: 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.

Speaker #1: This has come from multiple areas, sharper focus on our portfolio, yes, but then also optimizing our GNA. 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.

Speaker #1: And we're doing this two times faster. So instead of it taking the industry 42 months, we're seeing on average it takes us 17 months.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: A little bit more. Starting with our wholly owned pipeline. You know, look, when we look at the number of programs here, we have a diversified portfolio.

Speaker #1: There are different types of different across each of these programs. And I'm going to categorize it in three ways. Number one, there are programs where there's novel biological insight from our platform.

Speaker #1: 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, but there's significant unmet need that's still exists from a patient perspective.

Speaker #1: 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.

Speaker #1: This has come from multiple areas, sharper focus on our portfolio, yes, but then also optimizing our GNA. 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.

Speaker #1: So you've seen the 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 three categories.

Speaker #1: Starting with the platform-derived novel biological insight. All programs that exist in that category. One, FAP-4881, REC-4881. First of all, I don't need to say again, but like the reason why there's such a significant unmet need.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: There's nothing approved for these patients. This is a disease that is hallmarked by hundreds of polyps. Each and every one of which is precancerous.

Speaker #1: There are different types of differentiation across each of these programs. And I'm going to categorize it in three ways. Number one, there are programs where it's novel biological insight from our platform.

Speaker #1: And as a 100% risk of CRC colorectal cancer by the time you're 40. More than 50,000 addressable patients in the US and EU. The recursion differentiation is using the phenomena, the early version of the phenomena platform, to ascertain in an unbiased fashion that MEC-1/2 inhibition could actually work in FAP.

Speaker #1: 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 significant unmet need that still exists from a patient perspective.

Speaker #1: We have just completed our phase two study. We had a positive clean 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.

Speaker #1: So you've seen the slide before. We always track which components of our platform we are using across our various programs. So let's dive into a little bit more around the three categories.

Speaker #1: And one of our core next steps, and we're on track, is to initiate FDA engagement on the registration path, first half of 2026. We also have another program that is similar elements from a differentiation perspective, RBM-39.

Speaker #1: Starting with the platform-derived novel biological insight. All right. Two programs that exist in that category. One, FAP-4881, REC-4881. First of all, I don't need to say again, but the reason why there's such a significant unmet need.

Speaker #1: RBM-39, look, is going to be potentially important in genomically unstable cancers. And from the patient population as you can see that, that impacts a wide patient population.

Speaker #1: There's nothing approved for these patients. This is a disease that is hallmarked by hundreds of polyps. Each and every one of which is precancerous.

Speaker #1: The differentiation for recursion in our platform really came from uncovering this MOA and the connection it has to CDK12, which is known to be important for DDR modulation.

Speaker #1: And as 100% risk of CRC—colorectal cancer—by the time you're 40. More than 50,000 addressable patients in the US and EU. The Recursion differentiation is using the phenom[en]s, the early version of the phenom[en]s platform, to ascertain in an unbiased fashion that MET-1,2 inhibition could actually work in FAP.

Speaker #1: For many decades, the challenging to target because of the similar homology with CDK13. Right now, that program is in phase one monotherapy dose escalation.

Speaker #1: And we expect to share an early phase one update on safety and PK, first half, of 2026. So later half of this year. All right.

Speaker #1: We have just completed our phase two study. We had a positive clean a 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.

Speaker #1: 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.

Speaker #1: So let's start with CDK7. Look, 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.

Speaker #1: RBM-39, look, is going to be potentially important in genomically unstable cancers. And from the patient population, as you can see, that impacts a wide patient population.

Speaker #1: Which are with a wide variety of patient populations that are addressable, given a centrality in oncology. From a recursion differentiation perspective, others have tried this target before.

Speaker #1: The differentiation for recursion in our platform really came from uncovering this MOA and the connection it has to CDK12, which is known to be important for DDR modulation.

Speaker #1: And one of the key challenges has been optimizing the PK/PD, optimizing that therapeutic index. That's where we have leveraged it. The second element of our platform, AI chemistry, in order to optimize the molecule, especially around gut permeability.

Speaker #1: For many decades, the challenging to target because of the similar homology with CDK13. Right now, that program is in phase one monotherapy dose escalation.

Speaker #1: And we expect to share an early phase one update on safety and PK, first half, of 2026. So later half of this year. All right.

Speaker #1: We also are leveraging our platform in order to figure out which patient populations should be going to that could potentially impact the most from CDK7 inhibition.

Speaker #1: Progress right now, we finished our phase one 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.

Speaker #1: So let's start with CDK7. Look, 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, which are with a wide variety of patient populations that are addressable, given a centrality in oncology.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: From a recursion differentiation perspective, others have tried this target before. And one of the key challenges has been optimizing the PK/PD, optimizing that therapeutic index.

Speaker #1: 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.

Speaker #1: That's where we have leveraged it. The second element of our platform, AI chemistry, is used in order to optimize the molecule, especially around gut permeability. We are also leveraging our platform in order to figure out which patient populations we should be going to that could potentially benefit the most from CDK7 inhibition.

Speaker #1: 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 burden patient burden in terms of injections, subcutaneous, sometimes multiple a week.

Speaker #1: So what we wanted to do is design a molecule for ENPP1, which again, challenging target, especially in this space for hyperphosphatasia. Which can be suitable for chronic dosing.

Speaker #1: With more data expected in the 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.

Speaker #1: 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.

Speaker #1: All right. The next program that's also in this category is focused on ENPP1. ENPP1 loss or a certain mutation leads to challenges with bone mineralization, thereby leading to challenges in fractures, pain, et cetera.

Speaker #1: 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 MALT-1.

Speaker #1: MALT-1 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 a way 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.

Speaker #1: 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 injections—subcutaneous, sometimes multiple a week.

Speaker #1: So we have phase one monotherapy dose escalation ongoing. With early phase one update data, again, on safety and PK, monotherapy expecting first half of 2027.

Speaker #1: So what we wanted to do is design a molecule for ENPP1, which again, challenging target, especially in this space for hyperphosphatasia, which can be suitable for chronic dosing.

Speaker #1: Another program that is a similar theme, is LSD-1. LSD-1 is known to be an epigenetic regulator, really trying to prevent or inhibit some of the D differentiation that you see in solid tumors, such as small cell lung cancer and also AML.

Speaker #1: IND-enabling studies are ongoing for this program right now, and we expect to have a go/no-go decision in the second half of this year on this program. All right.

Speaker #1: 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.

Speaker #1: 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 MALT-1.

Speaker #1: This too, phase one monotherapy dose escalation is in startup. And next steps is to have early phase one update on safety and PK monotherapy, expected second half of 2027.

Speaker #1: 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.

Speaker #1: So we have phase one monotherapy dose escalation ongoing. With early phase one update data, again, on safety and PK, monotherapy expecting first half of 2027.

Speaker #1: And another program that's in preclinical and late, late preclinical is our PI3K1047 mutant selected. PI3K in general is an important oncogenic mutation linked to resistance and relapse, et cetera.

Speaker #1: Another program that is a similar theme is LSD-1. LSD-1 is known to be an epigenetic regulator, really trying to prevent or inhibit some of the D differentiation that you see in solid tumors, such as small cell lung cancer and also AML, with some validated data seen in AML recently.

Speaker #1: 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 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.

Speaker #1: But that's an IND enabling study. Again, go-no-go decision second half of this year, expected before we consider a phase one 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 stages, which is REC4881.

Speaker #1: 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 REC4881.

Speaker #1: I'm just going to do a quick update here. For this program, we had our clinical POC late, late last year. And a couple of things to note.

Speaker #1: No approved therapies. What we saw in our phase two three months on treatment with 4 milligram QG of this MEC-12 inhibitor, significant polyp burden reduction, about 43% median.

Speaker #1: 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 MEC-12 inhibitors, majority were grade 1, 2, RASH, CPK.

Speaker #1: 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 three months.

Speaker #1: And remember, this is a chronic disease. So the on-off element is going to be really important for us to understand. And we were the first to actually look at on and off in this disease area.

Speaker #1: 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, 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?

Speaker #1: So I won't recap in terms of the insight to proof point, but I'll focus on what's next. We're on track as we discussed late last year in terms of the FTA engagement initiating that first half of 2026, to really discuss the registrational study design.

Neerja Khan: on DNA study. Again, go, no-go decision, second half of this year expected, before we consider a phase I initiation. I know that was a rounded 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 in entering our clinical pipeline, which is our PI3K program. Let's go through the REC-4881. I'm just going to do a quick update here. You know, for this program, we had our clinical POC late last year. A couple of things to note. No approved therapies. What we saw in our phase II, 3 months on treatment with 4 mg QD of this MEK1/2 inhibitor, significant polyp burden reduction, at 43% median.

Najat Khan: on DNA study. Again, go, no-go decision, second half of this year expected, before we consider a phase I initiation. I know that was a rounded 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 in entering our clinical pipeline, which is our PI3K program. Let's go through the REC-4881. I'm just going to do a quick update here. You know, for this program, we had our clinical POC late last year. A couple of things to note. No approved therapies. What we saw in our phase II, 3 months on treatment with 4 mg QD of this MEK1/2 inhibitor, significant polyp burden reduction, at 43% median.

Speaker #1: 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.

Speaker #1: 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 in the last slide.

Speaker #1: 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.

Speaker #1: This is our PI3K1047 mutant selected. So look, for PI3K, I'm sure you're thinking, "Jack, there are multiple PI3K. Why are we working on PI3K?" First of all, this is a very, very important target across multiple solid tumors.

So let's go through the recorded 1. I'm just going to do a quick update here, you know? Um, for this program we had our clinical POC late late last year and a couple of things to note.

Neerja Khan: 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 from 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.

Najat Khan: 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 from 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.

Speaker #1: The current PI3K inhibitors have been constrained and we have some data that we'll share in shortly, hyperglycemia, metabolic toxicity, dose interruptions, dose reductions, limited treatment duration, all of that means is there an opportunity to do better by patients?

No approved therapies. Well, we saw in our Phase 2 3 months on treatments with 4 milligrams, QD of this Mech 1 2. Inhibitor significant pilot burden reduction about 43% medium, highest 1 of the higher burden, reductions to date 75% of the patients responded.

Speaker #1: There's an unmet need that still exists. So what is our differentiation and what is our thesis? It's really focusing on the 1047 mutant selective, which has 100x more selectivity over wild type.

In terms of the AES, that we see very much in line with what you see for MAC 1 to inhibitors—majority were grade 1, 2; rash, CPK, and no grade 4, 5 to date.

Speaker #1: 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.

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 a really important for us to understand and we're the first to actually look at on and off. In in this disease area, we see continued durable Halo, burden reduction, in some cases, actually deepening. And with a significant amount of the patients actually responding

Neerja Khan: This is a really important, you know, 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? I won't recap in terms of the insight to proof point, but I'll focus on what's next. You know, we're 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.

Najat Khan: This is a really important, you know, 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? I won't recap in terms of the insight to proof point, but I'll focus on what's next. You know, we're 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.

Speaker #1: For this program, we started off with X-ray structures that where we had proprietary structural insight. And that led us to leveraging our ND simulations.

So this is a really important, you know, 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.

Speaker #1: 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.

Speaker #1: 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.

Neerja Khan: As you remember, some of the data we shared was for 55 and over, we're already progressing on the 18 and over, and also advancing dose optimization efforts, really inspired by what we saw with the durability data that I shared on the last slide. We expect to have additional clinical data first half of 2027, as well. Stay tuned, more to come. Now, let's move to another exciting program that we have in our pipeline. This is our PI3K H1047R mutant selective. Look, for PI3K, I'm sure you're thinking, Jacques, 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.

Najat Khan: As you remember, some of the data we shared was for 55 and over, we're already progressing on the 18 and over, and also advancing dose optimization efforts, really inspired by what we saw with the durability data that I shared on the last slide. We expect to have additional clinical data first half of 2027, as well. Stay tuned, more to come. Now, let's move to another exciting program that we have in our pipeline. This is our PI3K H1047R mutant selective. Look, for PI3K, I'm sure you're thinking, Jacques, 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.

Speaker #1: Remember, it is that selectivity that leads to the tolerability challenges we talked about. And I want to take a moment, just look at the lower bar here.

So, I will recap in terms of the insight to proof point, but I'll focus on what's next. You know, we're on track as we discussed late last year, in terms of the FTA engagement, initiating that first half of 2026 to really discuss the registration of study designs. In addition to that, we have already started the enrollment of the 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 those optimization efforts, really in spite of what we saw with the durability data that I shared on the last slide.

Speaker #1: 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 better?

So we expect to have additional clinical data first half of 2027 um 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 Newton selected.

Speaker #1: 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.

So, look for PI3K. I'm sure you're thinking there are multiple PI3Ks. Why are we working on PI3K? First of all,

Speaker #1: 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?

Speaker #1: 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.

Neerja Khan: All of that means, is there an opportunity to do better by patients? There's an unmet need that still exists. What is our differentiation and what is our thesis? Is really focusing on the H1047R mutant selective, which has 100x more selectivity over wild type, thereby having the potential to minimize risk for AEs. In order to do that, we designed a molecule that can allow us to have that exclusive selectivity. 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 structures, that where we had proprietary structural insight. You know, that led us to leveraging our MD simulations, and this is where compute becomes really important. Our Molecular Dynamics simulations revealed a novel pocket.

Najat Khan: All of that means, is there an opportunity to do better by patients? There's an unmet need that still exists. What is our differentiation and what is our thesis? Is really focusing on the H1047R mutant selective, which has 100x more selectivity over wild type, thereby having the potential to minimize risk for AEs. In order to do that, we designed a molecule that can allow us to have that exclusive selectivity. 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 structures, that where we had proprietary structural insight. You know, that led us to leveraging our MD simulations, and this is where compute becomes really important. Our Molecular Dynamics simulations revealed a novel pocket.

This is a very, very important Target across multiple solid tumors. The current Pi we get Inhibitors, have been constrained and we have some data that will 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.

Speaker #1: And we actually also looked at some of the compounds that are in the market, such as PICRA. And also SCORPIENS compound, just to get a sense of how we're doing.

So what is our differentiation? And what is our thesis is really focusing on the 1047 mutant selective which has a 100x, more selectivity of a wild type. Thereby having the potential to minimize risk for AES.

Speaker #1: And we see significant tumor regression, not just reduction, but regression with this compound. Comparable to what you see with SCORPIEN and much better than what you see with PICRA.

And in order to do that, we designed a molecule that is going to allow us to have that exclusive selectivity.

Speaker #1: 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 converse standard of care today.

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 um structures that where we had proprietary structural insight.

Speaker #1: 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.

And you know that led us to leveraging our MD simulations and this is what compute becomes really important.

Our molecular Dynamics simulations revealed a novel.

Neerja Khan: We then used our generative 3D modeling efforts and machine learning in order to design molecules, novel scaffolds for this novel pocket. We were able to use other approaches, our other ML approaches, to really rapidly design our cycles to get exclusive potency, but also selectivity. Remember, it is that selectivity that leads to the tolerability challenges we talked about. I want to take a moment, like, 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? This is what we're tracking across our entire portfolio. I can tell you, compared to industry standards, this is fast.

Najat Khan: We then used our generative 3D modeling efforts and machine learning in order to design molecules, novel scaffolds for this novel pocket. We were able to use other approaches, our other ML approaches, to really rapidly design our cycles to get exclusive potency, but also selectivity. Remember, it is that selectivity that leads to the tolerability challenges we talked about. I want to take a moment, like, 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? This is what we're tracking across our entire portfolio. I can tell you, compared to industry standards, this is fast.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 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.

We then use 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 machine learning approaches really helped us to rapidly design our cycles. So, you get exquisite potency but then also selectivity. Remember, it's that selectivity that leads to the tolerability challenge that we talked about.

and I want to take a moment like,

Just look at the lower bar here in order to design this compound.

Speaker #1: 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 SCORPIEN and PICRA as well.

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?

Speaker #1: 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.

Compared to Industry standards business.

Neerja Khan: This is what gets us excited, data that we can actually do things better, faster. The next question is: How does this molecule do? I'll share some preclinical data that we haven't shared before. First, let's look at how it does from a tumor regression perspective. If you look at the left-hand side over here, well, what you're looking at here is a dose-dependent tumor regression for our compound, which is in blue. 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. 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.

Najat Khan: This is what gets us excited, data that we can actually do things better, faster. The next question is: How does this molecule do? I'll share some preclinical data that we haven't shared before. First, let's look at how it does from a tumor regression perspective. If you look at the left-hand side over here, well, what you're looking at here is a dose-dependent tumor regression for our compound, which is in blue. 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. 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.

And this is what gets us excited.

Data that we can actually do things better faster.

But then the the next question is, how does this molecule do? So, I'll share some pre-clinical data if we haven't shared before.

Speaker #1: Versus SCORPIEN and PICRA as well. So again, taken together, this is encouraging. But like I always say, the rubber hits the road in the clinic.

Speaker #1: 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 percent experience hyperglycemia.

Speaker #1: Large percent actually also have dose interruptions, dose reductions, some of those driven by the hyperglycemia. They're experiencing. And we also did some real-world analysis as well, given our clinical development AI platform, really thinking about what the target product profile could look like.

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 what you're looking at here is a dose dependent tumor regression for our compound as you did, which is in blue. And we actually also looked at some of the compounds that are in the market such as pick and also scorpions compounds just to get a sense of how we're doing and we see significant, tumor regression, not just reduction, but regression with this compound.

Neerja Khan: Given the standard of care, we also wanted to see the performance versus standard of care. With SURGE, with CDK4/6 inhibitors, which is that come as a standard of care today. 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 Tarpeet. We saw improved tumor regression with low dose of our asset versus high dose Tarpeet. All in all, this is encouraging from a efficacy perspective for this compound. We also wanted to look at tolerability.

Comparable to what you see with Scorpion, and much better than what you see with the pick rate.

Najat Khan: Given the standard of care, we also wanted to see the performance versus standard of care. With SURGE, with CDK4/6 inhibitors, which is that come as a standard of care today. 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 Tarpeet. We saw improved tumor regression with low dose of our asset versus high dose Tarpeet. All in all, this is encouraging from a efficacy perspective for this compound. We also wanted to look at tolerability.

Speaker #1: And you see the discontinuation about three to six months. 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 settings, so that's where we are today.

Speaker #1: It can be expanded at patient population in twofold. Number one, in breast cancer, not just in patients that are non-diabetic, but also patients that are pre-diabetic and diabetic.

Speaker #1: If this trajectory of hyperglycemia markers and not having impact holds. That's about 50/50 percent. In breast cancer. And then the other is there's also a broader patient population such as colorectal and endometrial.

But given the standard of care, we also wanted to see the performance versus standard of care. So it serves the cdk4 6 and Inhibitors which is that comma is 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 which 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 parking and we saw improved tumor regression with low dose of our asset versus high dose tests.

So all in all, this is an encouraging from a efficacy perspective for this compound.

Speaker #1: We can also explore. And one thing I'd be interested to also look at is, can this patients, because of the better tolerability, stay on longer?

But then, we also wanted to look at tolerability.

Neerja Khan: Here what you're seeing is animal models for, 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. 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. Again, taken together, this is encouraging, but like I always say, the rubber hits the road in the clinic. What does this mean from a clinic perspective?

Najat Khan: Here what you're seeing is animal models for, 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. 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. Again, taken together, this is encouraging, but like I always say, the rubber hits the road in the clinic. What does this mean from a clinic perspective?

Speaker #1: Longer treatment duration to really maximize the impact of these therapies. But again, clinical validation of improved tolerability requires is critical to confirm this expansion thesis.

Speaker #1: So if you go to the next slide, more to come. But again, we keep looking at these arcs. What was the insight? What did we design the molecule?

So here, what you're seeing is animal models for 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 hypoglycemia matter. Markers in naive wild type mice versus what you see with scorpion and pig for as well.

Speaker #1: What are the early proof points so far that you saw with preclinical data and what's next right now is a go-no-go decision for phase one which would be second half of this year.

Speaker #1: So currently, the study is in IND. All right. That was just our first pillar, double-click. We'll also do a little bit more around our partnerships and really excited to share the progress we're making.

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 hypoglycemia or the metabolic liability? Even at Supra efficacious dose for our assets.

Speaker #1: 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.

Neerja Khan: Look, current PI3K inhibitors, you know, focusing on HR-positive breast cancer, they do have tolerability limitations, you know. 65% to 85% experience hyperglycemia. Large percent actually also have dose interruptions, dose reduction, some of those driven by the hyperglycemia they're experiencing. We also did some, you know, real-world analysis as well, given our clinical development AI platform, like, really thinking about what the target product profile could look like. You see the discontinuation about 3 to 6 months, and that's not a very long time. 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 2-fold?

Najat Khan: Look, current PI3K inhibitors, you know, focusing on HR-positive breast cancer, they do have tolerability limitations, you know. 65% to 85% experience hyperglycemia. Large percent actually also have dose interruptions, dose reduction, some of those driven by the hyperglycemia they're experiencing. We also did some, you know, real-world analysis as well, given our clinical development AI platform, like, really thinking about what the target product profile could look like. You see the discontinuation about 3 to 6 months, and that's not a very long time. 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 2-fold?

Speaker #1: An actual program. To date, we have already achieved over 500 million in total cash inflows from our partnerships, both upfront and milestones. And we've actually laid out some of those recent ones with the momentum that we've been achieving recently.

Versus Scorpion and, and picra as well. So, again, taken together this is encouragement, but, like, I always say the rubber hits the road in the clinic. So, what does this mean? From a clinic perspective? Look, current P, speak Inhibitors. You know, focusing on HR positive, breast cancer. They do have tolerability limitations, you know,

Speaker #1: But I want to emphasize something that sometimes gets lost. Each and every one of the programs, that we're working on, has a potential for over 300 million in milestones and tiered royalty per small molecule program.

Speaker #1: 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 joined portfolio with Sanofi.

Neerja Khan: Number one, in breast cancer, not just in patients that are non-diabetic, but also patients that are pre-diabetic and diabetic, if this trajectory of hyperglycemia markers are not having impact holds. That's about 50 percent in breast cancer. The other is there's also broader patient populations such as colorectal and endometrial we can also explore. One thing I'd be interested to also look at is, can the patients, because of the better tolerability, stay on longer treatment duration to really maximize the impact of these therapies? Again, clinical validation and improved tolerability is critical to confirm this expansion thesis. If you go to the next slide, more to come, but again, we keep looking at these arcs. What was the insight? What do we design the molecule?

Najat Khan: Number one, in breast cancer, not just in patients that are non-diabetic, but also patients that are pre-diabetic and diabetic, if this trajectory of hyperglycemia markers are not having impact holds. That's about 50 percent in breast cancer. The other is there's also broader patient populations such as colorectal and endometrial we can also explore. One thing I'd be interested to also look at is, can the patients, because of the better tolerability, stay on longer treatment duration to really maximize the impact of these therapies? Again, clinical validation and improved tolerability is critical to confirm this expansion thesis. If you go to the next slide, more to come, but again, we keep looking at these arcs. What was the insight? What do we design the molecule?

Speaker #1: I mean, Sanofi has been a fabulous, fabulous partner. We learned so much from that exceptional team, both across INI and oncology. And what we're showing here is the multiple programs that we're working on, five and with multiple early discovery programs as well.

Speaker #1: And you see just like our internal pipeline, this is also a diversified pipeline. It's focused on challenging targets in INI 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.

65 to 85% experience, hypoglycemia large percent actually also have those interruptions. Those reductions some of those driven by the hypoglycemia they're experiencing. And we also did some, you know, real world analysis as well, given our clinical development, AI platform. Like really thinking about what the target product profile should look like. And you see the discontinuation about 3 to 6 months. 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 settings. So that's what we have today. It can be expand that patient population in 2 in breast cancer. Not just in patients that are non-diabetic but also patients that are pre-diabetic and diabetic if this trajectory of hyperglycemia markers and not having impacts owns, that's about 50/50 percent, um, in breast cancer. And then the other is there's also a broader patient population such as col and endometrial. We can also Expose and 1 thing that

Be interested to also look at is can the patients because of the better? Tolerability stay on longer longer treatment duration to really maximize the impact of these Therapies.

Speaker #1: So thinking with the clinic, the end in mind. And to date, we have advanced five lead packages that have been delivered by recursion across five of these programs and accepted by Sanofi to date.

Neerja Khan: What are the early proof points so far that you saw with preclinical data? What's next right now is a go, no-go decision for phase I, which would be second half of this year. Currently the study is in IND. All right. That was just our first pillar, DoubleClick. 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 up-fronts and milestones, and we've actually laid out some of those recent ones with the momentum that we've been achieving recently.

Najat Khan: What are the early proof points so far that you saw with preclinical data? What's next right now is a go, no-go decision for phase I, which would be second half of this year. Currently the study is in IND. All right. That was just our first pillar, DoubleClick. 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 up-fronts and milestones, and we've actually laid out some of those recent ones with the momentum that we've been achieving recently.

Speaker #1: 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.

But again, clinical validation of improved tolerability is critical to confirm this extension thesis. So if you go to the next slide, more to come, but again, we keep looking at these arcs: What was the insight? What did we design? The molecule, what are the early proof points so far that you saw with preclinical data, and what's next? Right now, it's a go/no-go decision for Phase 1, which would be in the second half of this year. So currently, the study is an IND.

Speaker #1: 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.

Speaker #1: 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.

All right, that was just our first pillar, double click. We'll also do a little bit more around our partnership. Some really excited to share the progress that you're making because remember proof points can come from most of your internal portfolio, which is what we just focused on. But then also from our amazing partners that we're working with an actual program,

Speaker #1: 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.

Neerja Khan: I wanna emphasize something that sometimes gets lost. Each and every one of the programs that we're working on has a potential for over $300 million in milestones and tiered royalty per small molecule program. Some of the royalties are up to double-digit royalties. 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 partner. We learned so much from that exceptional team, both across INI and oncology. What we're showing here is the multiple programs that we're working on, five, and with multiple early discovery programs as well. You see, just like our internal pipeline, this is also a diversified pipeline.

Najat Khan: I wanna emphasize something that sometimes gets lost. Each and every one of the programs that we're working on has a potential for over $300 million in milestones and tiered royalty per small molecule program. Some of the royalties are up to double-digit royalties. 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 partner. We learned so much from that exceptional team, both across INI and oncology. What we're showing here is the multiple programs that we're working on, five, and with multiple early discovery programs as well. You see, just like our internal pipeline, this is also a diversified pipeline.

But I want to emphasize something that sometimes just lost each and every 1 of the programs that we're working on.

Speaker #1: 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.

Speaker #1: 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 is our fifth milestone that we just achieved in the oncology program, really focused on leveraging A, these are targets that are data poor.

There is a 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 represents significant economics, and also a strong validation opportunity for Recursion.

All right.

Speaker #1: 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 pockets, and then leverage our machine learning algorithms in order to rapidly do our design-make-test cycle and find highly potent molecules that are now progressing to the next stage.

So very very excited for the first time today to unveil our joint portfolio with senosi. Miss senosi has been a fabulous, fabulous partner, we learned so much from that. Exceptional team both across ini and an oncology

Neerja Khan: It's focused on challenging targets in INI1 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. We're thinking with the clinic in mind. 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. I just wanna say we have a lot of important work ahead of us, with later stage discovery milestones over the next 18 months. Look, discovery is probabilistic.

Najat Khan: It's focused on challenging targets in INI1 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. We're thinking with the clinic in mind. 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. I just wanna say we have a lot of important work ahead of us, with later stage discovery milestones over the next 18 months. Look, discovery is probabilistic.

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 ini and oncology with molecules that have the potential to be first in class and or Best in Class.

Speaker #1: Very exciting progress here. And stay tuned for 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.

With programs that address, very specific unmet needs. So we're thinking with the clinic and in mind,

Speaker #1: 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 three components of our platform, starting with biology to insight.

And to date, we have, um, advanced 5 lead packages that have been delivered by Recursion across 5 of these programs and accepted by Sanofi today.

That's about 34 million in Milestones today, in addition to the 100 million in upfront. So 134 million so far.

Speaker #1: 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.

Neerja Khan: 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. Just double-clicking on one of these, you know, how do we get there? Remember, you know, these are challenging targets, and we are leveraging our platform. I just wanna explain one aspect that I think is really important. You know, our platform is not about one data, one model, one answer. It's about the confluence of a suite of them that you use for the problem at hand.

Najat Khan: 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. Just double-clicking on one of these, you know, how do we get there? Remember, you know, these are challenging targets, and we are leveraging our platform. I just wanna explain one aspect that I think is really important. You know, our platform is not about one data, one model, one answer. It's about the confluence of a suite of them that you use for the problem at hand.

Speaker #1: 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 data that you need to then build foundation models that are state-of-the-art.

Speaker #1: 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.

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 probably, you know, 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 1 of these, you know, how do we get there? And remember, you know, these are challenging targets and we are leveraging our platform and I just want to explain 1 aspect that I think is really important.

Speaker #1: Genetics, transcriptomic, proteomic, phenomic, patient data. That's the effort that we're focused on. And how do we leverage it? That's the so what is what matters.

You know, our platform is not about 1 data, 1 model 1 asset. It's about the Confluence of a suite of events that you use for

Neerja Khan: We start with the problem first, and then you have flexibility and optionality across our models to get to the best outcome. Again, our latest program, where we just got a milestone, our 5th milestone that we just achieved in the oncology program, really focused on leveraging, A, these are targets that are data poor. 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, a novel pocket, and then leverage our machine learning algorithm in order to rapidly do our design, make 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.

Najat Khan: We start with the problem first, and then you have flexibility and optionality across our models to get to the best outcome. Again, our latest program, where we just got a milestone, our 5th milestone that we just achieved in the oncology program, really focused on leveraging, A, these are targets that are data poor. 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, a novel pocket, and then leverage our machine learning algorithm in order to rapidly do our design, make 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.

the problem at,

Speaker #1: 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.

Speaker #1: 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 can tell you this is harder than it looks.

Hand to be served 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 Milestones are 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.

Speaker #1: And we have used our Insilico approaches to generate over 100 million molecules. One emphasis, one point I want to emphasize is the point around synthetically aware design.

Speaker #1: 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.

And the protein flexibility, better find, novel Target, a novel pocket. And then leverage our machine learning um algorithms in order to rapidly do our design make test cycle and find highly potent molecules, um, that are now progressing to the next stage.

Neerja Khan: This is truly what proof points look like, actually showing value that will matter for the medicines that we are working towards. Look, none of this can happen without a unique and differentiated platform, that is an ever important work in progress. I want to just do a snapshot of the three components of our platform, starting with biology to insight. You know, I mentioned about the proprietary data that Precision has been building for a decade, over 50 petabytes of high-quality multimodal data, and I want to emphasize the multimodal piece. You know, biology is complex, and having diversity of data and, you know, 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 phenomics foundation models that are state-of-the-art.

Najat Khan: This is truly what proof points look like, actually showing value that will matter for the medicines that we are working towards. Look, none of this can happen without a unique and differentiated platform, that is an ever important work in progress. I want to just do a snapshot of the three components of our platform, starting with biology to insight. You know, I mentioned about the proprietary data that Precision has been building for a decade, over 50 petabytes of high-quality multimodal data, and I want to emphasize the multimodal piece. You know, biology is complex, and having diversity of data and, you know, 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 phenomics foundation models that are state-of-the-art.

Very exciting progress here, and stay tuned more to come.

Speaker #1: So we always start with that end in mind, that target product profile. What can be a true drug that matters? We do that across our partnerships and our internal portfolio.

But this is truly what proof points look like—actually showing value, double matter for the medicines that we are working towards. But look,

Speaker #1: And like I said before, 90% of these molecules are generated for prioritized by our models. And one thing that we're doing increasingly, not just leveraging automation, but also agentic orchestration.

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 incite.

Speaker #1: So we can get things done better, faster, in a more unbiased approach. And I mentioned the stat before, but I can't wait to mention it again.

Speaker #1: Look, we on average, across the portfolio, so with PIGK, you said 242 compounds. 10 months. But across the portfolio, we like to be transparent around our data.

Speaker #1: 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.

Neerja Khan: We have a fantastic team that's working on this, whether it's the phenomics foundation models or the transcriptomic foundation models. Combining those is a fusion of those models that are going to be 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. How do we leverage it? That's the so what is what matters, is creating these novel proprietary data sets. We call them biology maps, and, you know, we have built internally across different therapeutic areas. We also have it in neuroscience and GI onc with Roche Genentech. 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.

Najat Khan: We have a fantastic team that's working on this, whether it's the phenomics foundation models or the transcriptomic foundation models. Combining those is a fusion of those models that are going to be 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. How do we leverage it? That's the so what is what matters, is creating these novel proprietary data sets. We call them biology maps, and, you know, we have built internally across different therapeutic areas. We also have it in neuroscience and GI onc with Roche Genentech. 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.

Speaker #1: 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.

You know, I mentioned about the proprietary data that this version has been building for a decade, 50 over 50, PYT of high quality multimodal data and I want to emphasize that multimo please. You know, biology is complex and having diversity of data and, you know, 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 the phenomics foundation models or the transcriptions transcriptomics 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.

Speaker #1: 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.

Speaker #1: 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.

Speaker #1: 300 million plus real-world live sets, through both some internal work, but then also this great ecosystem integrated data partnerships. We're very opportunistic around that.

Transcriptomic proteomic, phenomic patient data. That's the effort that we're focused on and how do we leverage it? That's the. So what is what matters is creating these novel proprietary data sets. We call them biology maps and you know, we have those internally across different therapeutic areas. We also have it in neuroscience and Gi Oh, with Roche. Genentech. And that's what those insights is. What's fueling, our Discovery pipeline.

Neerja Khan: You know, we have used our in silico approaches to generate over 100 million molecules. You know, 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. Like I said before, 90% of these molecules are generated or prioritized by our models.

Najat Khan: You know, we have used our in silico approaches to generate over 100 million molecules. You know, 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. Like I said before, 90% of these molecules are generated or prioritized by our models.

Speaker #1: So some of the early results, I mean, you can read the bullets here. For the one that I would point to the attention to is enrollment rates.

Speaker #1: 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, or some of the programs, we're starting to see 1.3 to 1.6x improvement.

Harder than it looks. And you know, we have used our insilico approaches to generate over 100 million molecules, you know, 1 point. I want to emphasize is the point around synthetically aware design.

and there's 1 thing to design molecules that are interesting, but if you cannot make them,

Speaker #1: We are also just improving the operational piece that goes underneath it in terms of just starting studies faster. By up to three months, all of this accumulates.

Speaker #1: 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.

Then that limits, or if you can make them but the CMC is very challenging, that really limits that end in mind. So we always start with that end in mind, that Target Product Profile—what can be a true drug? That matters. We do that across our partnerships and our internal portfolio.

Speaker #1: 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.

Neerja Khan: You know, 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. I mentioned this 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. 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. We do it in 17 months on average, versus 40 months+ for industry. These are the kinds of things that we track, and that's going from target all the way to advanced candidate.

Najat Khan: You know, 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. I mentioned this 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. 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. We do it in 17 months on average, versus 40 months+ for industry. These are the kinds of things that we track, and that's going from target all the way to advanced candidate.

Speaker #1: 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 US here, across the country.

And like I said before, 90% of these molecules are generated—sport prioritized by our plat, uh, by our models. You know, one thing that we're doing increasingly, not just leveraging automation, but also agentic orchestration, so we can get things done better, faster, in a more unbiased approach.

And I mentioned the stat before, but I can't.

Speaker #1: Then we go into deeper resolution at a state level, and then at a zip code, three-digit zip code level, and then at a site level.

Wait to mention it again. Um, but we we on average across the portfolio. So with PK you said 242 compounds.

Speaker #1: And what's really important here is we can also get data around the sites' experience. With running that trial. And this is you can probably guess for which program, ovarian cancer trials.

Speaker #1: 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.

Neerja Khan: 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. 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, 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, you know, through both some internal work, also the great ecosystem, integrated data partnerships. We're very opportunistic around that. Some of the early results, I mean, you can read the bullets here, but the one that I would point the attention to is enrollment rates.

Najat Khan: 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. 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, 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, you know, through both some internal work, also the great ecosystem, integrated data partnerships. We're very opportunistic around that. Some of the early results, I mean, you can read the bullets here, but the one that I would point the attention to is enrollment rates.

10 months, but across the portfolio, we like to be transparent around our data 330 compounds is what we synthesize on, average versus 2500 5,000 in industry. And we do it in 17 months on average versus 40 months plus our industry. This is, these are the kinds of things that we track and that's going from Target all the way to Advanced candidates.

Speaker #1: And then beyond that, we can also get 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.

And as a result, we have over 10 developers across our internal portfolio and getting to that line with our internal and partner programs as well.

Speaker #1: 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.

Speaker #1: 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.

Speaker #2: Thanks, Nujat. 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.

Neerja Khan: Look, in order to execute on programs, you have to enroll in a very efficient and intelligent way. In 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. You remember the point around the compounding impact of decisions across the platform? This is how you define drug discovery and development, leveraging AI. Let me just give you a sneak peek as to how that works on the enrollment front. We start with the 300 million patient lives.

Najat Khan: Look, in order to execute on programs, you have to enroll in a very efficient and intelligent way. In 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. You remember the point around the compounding impact of decisions across the platform? This is how you define drug discovery and development, leveraging AI. Let me just give you a sneak peek as to how that works on the enrollment front. We start with the 300 million patient lives.

And last but certainly not. The least is a 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 you know, 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 1 that I would point to the attention to is enrollment rate, but we are

Speaker #2: 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.

To in order to execute on programs, you have to enroll in a very efficient and intelligent way. And some of our early results are some of the programs, we're starting to see 1.3 to 1.66 Improvement.

Speaker #2: 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 five clinical programs, hitting multiple different partner milestones, really investing behind the growth in our platform as well.

We are also just improving the operational piece that goes underneath it in terms of just starting studies faster. Um, 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.

Neerja Khan: 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 US here, across the country. We can go into deeper resolution at a state level, and then at a zip code, three-digit zip code level, and then at a site level. What's really important here is we can also get data around the site's experience with running that trial. 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. Beyond that, we can also get how many patients do these sites have?

Najat Khan: 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 US here, across the country. We can go into deeper resolution at a state level, and then at a zip code, three-digit zip code level, and then at a site level. What's really important here is we can also get data around the site's experience with running that trial. 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. Beyond that, we can also get how many patients do these sites have?

Should we start with the 300 million patient lives?

Speaker #2: 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.

Speaker #2: And so that's how we were able to come back and have a 35% year-over-year reduction from portfolio 24 to 25, and even come in 10% below the guidance that we originally provided in May of last year.

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 US here across, um, the country then we can go into deeper resolution at a state level and then at a zip code, 31,

And what's really important here is, we can also get data around the site's experience.

With running that trial.

Speaker #2: So we ended the year with 754 million in cash, looking forward to our 2026 cash operating expenses. Our expected to be under 390 million.

And this is—you can probably get through which program, ovarian cancers, uh, 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.

Speaker #2: Cash operating expenses is a non-gap measure that we're going to be using to give you guidance. We have a lot of non-cash expenses in our P&L.

Neerja Khan: 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, because 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.

Najat Khan: 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, because 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.

Speaker #2: 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.

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 on 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

Speaker #2: 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.

I call this, we talk about Precision medicine, Precision, biology Precision chemistry. This is precision operations and starting with the patient in mind.

Speaker #2: 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.

Ben Taylor: Thanks, Neerja. 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. We looked at how every $ in the company goes towards a specific quantifiable outcome. That's how we were able to achieve the efficiencies that we did over the last year, while still advancing a portfolio of five clinical programs, hitting multiple different partner milestones, really investing behind the growth in our platform as well.

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, new John. So, 2025 was a year of financial transformation for the company.

Speaker #2: 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.

Speaker #2: 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 get to extend out our cash runway.

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.

Speaker #2: 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 Nujat.

And so we looked at how every dollar in the company goes towards a specific quantifiable outcome.

Speaker #3: 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.

Ben Taylor: All of that comes back to focus on those investments across our pipeline and technology portfolio that have the best risk return, that are gonna give us the most impact for the investment that we're making. That's how we were able to come back and have a 35% year-over-year reduction from pro forma 2024 to 2025, and even come in 10% below the guidance that we originally provided in May 2023. 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.

Speaker #3: 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.

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.

Speaker #3: 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.

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 ProForm read 24 to 25 and even come in 10% below the guidance that we originally provided in May of last year.

Speaker #3: We'll also have additional data for 4881 early next year. And then combo data expected for the RCDK7 program, as well as more early safety and PK data from MOLS1 and LSD1 recall for both of those.

So we ended the year with $750 million in cash. Looking forward, our 2026 cash operating expenses are expected to be under $390 million.

Ben Taylor: We have a lot of non-cash expenses in our P&L, we wanted to provide something that showed what our cash profile might look like going forward. 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. We do include a probability weighting of some of those milestones in our cash flow projections going forward.

Speaker #3: 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.

Speaker #3: Because that's going to be very important. Our partnerships with Sanofi, as we just discussed, in terms of multiple programs, have been progressing into more later-stage development candidate and other milestones as well.

Speaker #3: But in addition to that, these maps, these maps were novel biologies really would come from extracting that into new programs with Roche, Genentech, et cetera.

cash operating. Expenses is a non-gaap measure that we're going to be using to give you guidance. We have a lot of non-cash uh expenses in our p&l. And so we wanted to provide something that showed what our cash uh profile might look like going forward. And so this is coming directly off of our cash flow statement. If you look at um, operational cash flow and then you add back our inflows from partnership and transaction costs. You'll be able to get directly to this, uh, guidance number that we're using.

Uh, in addition, in last year, it was really exciting to see uh that we crossed the 500 million.

Speaker #3: 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.

Milestone in cumulative partner inflows—we expect to continue to achieve those going forward. And in fact, we hit our first milestone.

Speaker #3: And as Ben just mentioned, pairing all of that important work with discipline execution. We've really pivoted towards an outcomes-based budget, where we test what every dollar would value creation every dollar can drive.

Uh, earlier this month already. Uh, and so we do include a probability weighting of some of those milestones in our cash flow.

Ben Taylor: 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 get to extend out our cash runway. We're updating our guidance to go to early 2028 as of now. I will hand it back over to Neerja.

Actions going forward.

Speaker #3: 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.

That's actually the really exciting part for me is not only were we able to exceed our efficiency, expectations, but that actually means to extend out our cash rental,

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.

Speaker #3: 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.

Neerja Khan: 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 gonna be a busy next 18 to 24 months. We'll see if I can recover my voice soon. In terms of this year, you know, like I said, we're on track for our initial engagement with the FDA on REC-4881. We're looking forward to that. Also initial data, early safety, and then also PK for RBM39. Go, no-go decisions for PI3K and ENPP1, which are both in IND enabling. We'll also have additional data for REC-4881 early next year, and then combo data expected for our CDK7 program, as well as more early safety and PK data for MALT1 and LSD1.

Najat Khan: 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 gonna be a busy next 18 to 24 months. We'll see if I can recover my voice soon. In terms of this year, you know, like I said, we're on track for our initial engagement with the FDA on REC-4881. We're looking forward to that. Also initial data, early safety, and then also PK for RBM39. Go, no-go decisions for PI3K and ENPP1, which are both in IND enabling. We'll also have additional data for REC-4881 early next year, and then combo data expected for our CDK7 program, as well as more early safety and PK data for MALT1 and LSD1.

Thank you so much Ben.

we'll wrap it up by just

Speaker #3: And I'll also have our CFO, Dave Hallett, joining us as well. In addition to Ben Taylor, in order to address some other questions. All right.

Speaker #3: From Sean at Morgan Stanley and Priyanka as well, thank you for the questions from JPMorgan and from Brendan at Cowan. So many people. Questions around REC 4881.

Speaker #3: 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.

We have a very broad set of catalysts that are coming up and it's going to be a busy, a busy next 18 to 24 months. We'll see if I can recover my voice soon. Um, in terms of, uh, this year, uh, you know, like I said, we're on track for our initial engagement, but the FDA on recorded 1, was looking forward to that and also initial data early, safety. And, and then also pique for RBM 39.

And go. No, go decisions for PS. We can and P 1 which are both in IND enabling

Speaker #3: So 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.

Neerja Khan: Recall, for both of those, we designed the assets to be more tolerable. These are going to be important. 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. 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. In addition to that, you know, these maps, these maps where novel biology really would come from extracting that into new programs with Roche, Genentech, et cetera. Really, really important work that continues. 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.

Najat Khan: Recall, for both of those, we designed the assets to be more tolerable. These are going to be important. 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. 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. In addition to that, you know, these maps, these maps where novel biology really would come from extracting that into new programs with Roche, Genentech, et cetera. Really, really important work that continues. 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.

Speaker #3: 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 the 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.

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 MODS1 and LSD1 recall for both of those. We designed the, uh, the assets to be more tolerable. These are going to be important.

Speaker #3: 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 a really important for us to have conversations with the FDA.

Speaker #3: So that's point one. Point two is also around the updated patient populations. 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.

Speaker #3: 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.

Neerja Khan: As Ben just mentioned, you know, pairing all of that important work with disciplined execution. We've really pivoted towards an outcome-based budget, where we test what every dollar, what value creation every dollar can drive to doing more with less. I'll close by saying thank you so much for the time, and also, you know, our focus will always remain on value creation for patients. They're the ones that we ultimately serve. Patients are waiting, and also, of course, our shareholders. Thank you again for listening, and with that, I'm gonna pivot to the Q&A section, and I'll also have our CFO, Dave Hallett, joining us as well, in addition to Ben Taylor, in order to address some of the questions. From Sean at Morgan Stanley and Priyanka as well, thank you for the questions from JP Morgan and from Brendan at Cowen.

Najat Khan: As Ben just mentioned, you know, pairing all of that important work with disciplined execution. We've really pivoted towards an outcome-based budget, where we test what every dollar, what value creation every dollar can drive to doing more with less. I'll close by saying thank you so much for the time, and also, you know, our focus will always remain on value creation for patients. They're the ones that we ultimately serve. Patients are waiting, and also, of course, our shareholders. Thank you again for listening, and with that, I'm gonna pivot to the Q&A section, and I'll also have our CFO, Dave Hallett, joining us as well, in addition to Ben Taylor, in order to address some of the questions. From Sean at Morgan Stanley and Priyanka as well, thank you for the questions from JP Morgan and from Brendan at Cowen.

Speaker #3: We like to be real-time and transparent when we have more meaningful outcomes. And updates will absolutely be sharing with the street as well. All right.

Speaker #3: Next question is from Alex from Bank of America. It looks like the cost-cutting measures cost optimization. Cost-cutting measures really started to take hold in Q4.

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 senosi, as we just discussed in terms of multiple programs, as they're progressing into more later stage development candidate and other Milestones as well. But in addition to that, you know, these Maps, these Maps were novel, biology is really would come from extracting that into new programs which row genetic Etc. So really, really important work that 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 you know pairing all of that important work with discipline execution. Uh we really pivoted towards an outcome space budget where we test what every dollar would value creation every dollar can drive doing more with less, so a close by saying, thank you so much for the time.

Speaker #3: 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?

Speaker #2: Sure. Happy to. Yeah, thanks, Alex. So if you think about it, I agree with Nujat. 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.

And also, you know, our Focus will always remain on value creation for patients. They are the ones that be ultimately serve. Um, patients are waiting and also, 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 Hallet joining us as well. In addition to Ben Taylor, in order to address some of the questions.

Speaker #2: There's no big one-offs in the system. But what we really try and 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.

Neerja Khan: 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, you know, 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 gonna be really important in terms of discussing the potential design for registrational study, patient population, endpoints. You know, we have a very compelling data set in terms of the durability and then also polyburden 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.

Najat Khan: 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, you know, 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 gonna be really important in terms of discussing the potential design for registrational study, patient population, endpoints. You know, we have a very compelling data set in terms of the durability and then also polyburden 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.

Speaker #2: And so when you come in with that, 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.

On that for that, that's going to be really important in terms of discussing the potential design for registration of study.

Speaker #3: Great. Thank you, Ben. I mean, the only thing I'll add is, Alex, 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 is evolved as you generate more data.

Neerja Khan: Coupled with that, it's gonna be really important for us to have conversations with the FDA. That's point one. Point two, is also around the updated patient population. 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. More data on that coming, first half of 2024. 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 Alex from Bank of America.

Najat Khan: Coupled with that, it's gonna be really important for us to have conversations with the FDA. That's point one. Point two, is also around the updated patient population. 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. More data on that coming, first half of 2024. 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 Alex from Bank of America.

Speaker #3: 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.

Speaker #3: 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?

Population, endpoints, you know, we have a very compelling data set in terms of the durability and then also political and 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, is going to be really important for us to have conversations with the FDA. So that's Point 1 Point 2 um, is also around the updated patient population. So as I mentioned 18 and over that, uh, 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, um, first half of 2027,

Speaker #3: OK. I think it's going to be this great question. Thank you so much. It's going to be important to decouple two parts. One is the investment from NVIDIA.

Look, as we have meaningful updates across both fronts—as you've 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

Speaker #3: 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 high res really being the recursion being a pioneer in how to leverage automation, this wet and dry lab.

Neerja Khan: It looks like the cost-cutting measures, cost optimization, cost-cutting measures really started to take hold in Q4. Any one-off that helped in the quarter, or are these levels sort of the expectation for the go-forward? Ben, do you want to take that?

Najat Khan: It looks like the cost-cutting measures, cost optimization, cost-cutting measures really started to take hold in Q4. Any one-off that helped in the quarter, or are these levels sort of the expectation for the go-forward? Ben, do you want to take that?

America.

Ben Taylor: Sure, happy to. Yeah, thanks, Alex. If you think about it, I agree with you, Jiat, it's really about efficiency more than cost cutting. 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 on 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.

Speaker #3: 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 too, one of the fastest supercomputer and life sciences.

It looks like the cost cutting measures cost. Optimization cost cutting measures really started to take. Hold in Q4 any 1 off that helped in the quarter or are these levels sort of the expectation for the go forward? Then do you want to take that? Sure. Happy to uh yeah. Thanks Alex. So uh if you think about it I agree with your job. It's really about efficiency more than cost cutting. So we have hit a point where, uh,

We have.

Speaker #3: That I mentioned the example from 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.

Speaker #3: 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.

Ben Taylor: 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.

Gone through all of the integration, I would assume that that is all complete. There's no big 1 off in the system. But what we, uh, really try and do is come in with Attitude, where we want to continue to find ways for every dollar to make uh more of an impact in the following years and months than it did previously. And so when you come in with that uh that attitude all of a sudden you start to find ways to do more with less and that's where we'll, we expect to be able to continue growing our pipeline investing, uh, heavily behind our platform and moving things, forward while still hitting those cost targets that we put out there.

Speaker #3: And so that's really a 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.

Neerja Khan: Great. Thank you, Ben. I mean, the only thing I'll add is, Alex, 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 have 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 gonna be this great question. Thank you so much.

Najat Khan: Great. Thank you, Ben. I mean, the only thing I'll add is, Alex, 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 have 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 gonna be this great question. Thank you so much.

Okay. Um, thank you, Ben. I mean, the only thing I'll add is, Alec, I think it's—

Speaker #3: So those are to be two areas to be decoupled. The last thing I'll also say you also sorry, there's so many questions. Also ask a question.

Speaker #3: 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.

Piece around rapid go no-go decisions and how we are doing that. Just the mentality in 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 have evolved, as you generate more data, almost thinking, like an investor, I think is really important having agile around Capital allocation and that's what we will continue to do. Of course, being driven by data.

Speaker #3: 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.

Speaker #3: All right. We'll take one more question here. From George's with the recent positive preliminary efficacy data for REC4881 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?

Um, great next question. Uh, 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 etc. Etc. Okay.

Neerja Khan: It's going to be important to decouple two 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, 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, you know, I mentioned the example from PI3K, around Sanofi, using machine learning, using, you know, molecular dynamics. All of that is underpinned by our supercomputer.

Najat Khan: It's going to be important to decouple two 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, 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, you know, I mentioned the example from PI3K, around Sanofi, using machine learning, using, you know, molecular dynamics. All of that is underpinned by our supercomputer.

Speaker #3: 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 further.

Speaker #4: Thank you, Nujat. And kind of good morning and good afternoon to those of us in Europe. I think I'll maybe start from the discovery perspective, I think, Nujat.

Speaker #4: During the last presentation, it was highlighted a number of themes. One is about the repeatability of kind of delivery, I think. I think that's specifically highlighted in the burgeoning Sanofi kind of pipeline that we're kind of that we're building together.

Neerja Khan: Our partnership with NVIDIA couldn't be any stronger, so that continues. In terms of the divestment, you know, 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. That's really a 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 $1 billion plus investments. Those are to be two areas to be decoupled. The last thing I'll also say, sorry, there's so many questions. Also ask a question, are we seeking other technology partners?

Najat Khan: Our partnership with NVIDIA couldn't be any stronger, so that continues. In terms of the divestment, you know, 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. That's really a 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 $1 billion plus investments. Those are to be two areas to be decoupled. The last thing I'll also say, sorry, there's so many questions. Also ask a question, are we seeking other technology partners?

I think it's going to be this great question. Thank you so much. It's going to be important to decouple 2 parts. 1 is the investment from uh, Nvidia and 1 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 high res, 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 a bio High 2 of 1 of the fastest of the computer and life sciences that, you know, I mentioned the examples from pi3k around, send no fee using machine learning using um, you know, 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, you know, this really was if you look at the

Speaker #4: This is kind of a repeatable platform that's kind of delivering both best-in-class and kind of first-in-class challenging targets. Both JPMorgan and, again, this presentation, I think we've highlighted the speed of delivery.

Speaker #4: 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.

Neerja Khan: We have a strategic partnership with Google as well in terms of cloud compute. We have a partnership, as I mentioned, with NVIDIA on machine learning and models, et cetera, but also on-prem compute. We will continue. You know, 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 Georges 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?

Najat Khan: We have a strategic partnership with Google as well in terms of cloud compute. We have a partnership, as I mentioned, with NVIDIA on machine learning and models, et cetera, but also on-prem compute. We will continue. You know, 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 Georges 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?

Speaker #4: 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.

Speaker #4: And obviously, the goal over the coming months and years is to show repeatability in that frame as well.

Tech and Science and will continue to do that.

All right, um, we'll take 1 more question here.

Speaker #3: Thank you, Dave. And just to maybe add a little bit of a broader perspective, looking at recursion, five-plus clinical programs, a diversified portfolio in the clinic side, a diversified portfolio on the discovery side.

Speaker #3: 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 one-two asset biotech.

Neerja Khan: I'm gonna hand it over to David Hallett to get us started, and I'm sure we can also add some more comments as well.

Najat Khan: I'm gonna hand it over to David Hallett to get us started, and I'm sure we can also add some more comments as well.

Speaker #3: 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.

From Georgia's with the recent positive preliminary efficacy data for wreck 481 in fap and the achievement of your fifth Milestone with senosi, 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. Um, I'm going to hand it over to Dave, howlet to get us started and I'm sure we can also add someone comments.

Dave Hallett: Thank you, Jiat, good morning and good afternoon to those of us in Europe. I think I'll maybe start from the discovery perspective. I think.

Dave Hallett: Thank you, Jiat, good morning and good afternoon to those of us in Europe. I think I'll maybe start from the discovery perspective. I think during the last presentation has highlighted a number of themes. One is about the repeatability of kind of delivery. I think that's specifically highlighted in the burgeoning Sanofi kind of pipeline that we're kind of that we're building together. This is a kind of a repeatable platform that's kind of delivering both best-in-class and kind of first-in-class challenging targets. Above JP Morgan, and again, in this presentation, I think we've highlighted the speed of delivery.

Thank you, Nia and uh, kind of good morning and good afternoon to those of us in Europe. Um,

Speaker #3: Making all of this much more engineering-focused, using whether it's genetic agents or automations 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.

Jiat: During the last presentation has highlighted a number of themes. One is about the repeatability of kind of delivery. I think that's specifically highlighted in the burgeoning Sanofi kind of pipeline that we're kind of that we're building together. This is a kind of a repeatable platform that's kind of delivering both best-in-class and kind of first-in-class challenging targets. Above JP Morgan, and again, in this presentation, I think we've highlighted the speed of delivery.

I think I maybe start from um the discovery perspective, I think um the j-hat

Speaker #3: 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.

Speaker #3: 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 the space.

Speaker #3: 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 applying models that unlock value.

Jiat: 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 demonstrate that a compound that came from Recursion has shown clinical proof of concept. Obviously, the goal over the coming months and years is to just show repeatability in that frame as well.

Dave Hallett: 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 demonstrate that a compound that came from Recursion has shown clinical proof of concept. Obviously, the goal over the coming months and years is to just show repeatability in that frame as well.

During the last presentation is highlighted, a number of themes. Um, 1 is about the repeatability of of, kind of delivery. I think, I think that's specifically highlighted, um, in the, um, in the burgeoning sopi kind of pipeline that we're kind of that we're building together. Um, this is a, a kind of a repeatable platform, that's kind of delivering, um, both best-in-class and kind of first-in-class, challenging targets. Um, above JP Morgan. And again, this this presentation, I think we've highlighted, um, the speed of delivery. Um,

Speaker #3: 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 industry, the speed with which, the cost of our INDs.

If you look at the metrics that, that we're delivering in terms of numbers of Novel compounds that, um, that we synthesize and test and the speed that we're getting, um, to these development candidates. Um, these are, I think further demonstration that the role kind of Technology plays in kind of accelerating that delivery. Um,

Speaker #3: 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 and enrollment and so forth.

Speaker #3: 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.

Speaker #3: So thank you all for your support to our partners. To our shareholders, but most, most importantly, to patients that are willing to take a bet on us in our programs and that are waiting.

The proof is ultimately in the clinic. Um, and clearly we're very excited, um, for patients in terms of, um, FAP. I think this is the, um, the first example from our platform where we've been able to kind of demonstrate that a compound that came from Recursion has shown, uh, clinical proof of concept. Um, and obviously the goal over the coming months and years is to just show repeatability in that frame as well.

Neerja Khan: Thank you, Dave. Just to maybe add a little bit of a broader perspective, you know, looking at Recursion, five 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, you know, we are not a one, two asset biotech.

Najat Khan: Thank you, Dave. Just to maybe add a little bit of a broader perspective, you know, looking at Recursion, five 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, you know, we are not a one, two asset biotech.

Thank you Dave. And and just

Speaker #3: 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.

Neerja Khan: We are a TechBio 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, you know, making all of this much more engineering focus, using, whether it's genetic agents or automations, to do things better and faster, taking toil out of the system so we can supercharge our scientists more and more to do the hard work. I just wanna 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. We know that multiple shots on goals is gonna be important.

Najat Khan: We are a TechBio 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, you know, making all of this much more engineering focus, using, whether it's genetic agents or automations, to do things better and faster, taking toil out of the system so we can supercharge our scientists more and more to do the hard work. I just wanna 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. We know that multiple shots on goals is gonna be important.

Conversion 5 um plus clinical programs and diversified portfolio on the clinic side. I 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. Um, all of that, I just think taking a big step back. You know. We are not a 1 to fit biotech um and we are a tech bio for a reason which is the piece that they just mentioned really well, which is what we're really trying to focus on is the repeatability, the scalability, you know, making all of this much more engineering.

Neerja Khan: That's the kind of fortitude and resilience that's needed in this space, and we're adding an area, two worlds coming together in tech and bio, that haven't really come together before. Not just building models that are interesting, but actually applying models that unlock value. 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've done such, 90% less than industry, the speed with which, the cost of our IND, we do the same thing in the biology platform. We do the same thing with the clinical development, as you saw me share, you know, where we're seeing improvement in enrollment and so forth. There's so much work to be done, but this is what, quite frankly, gets us excited.

Najat Khan: That's the kind of fortitude and resilience that's needed in this space, and we're adding an area, two worlds coming together in tech and bio, that haven't really come together before. Not just building models that are interesting, but actually applying models that unlock value. 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've done such, 90% less than industry, the speed with which, the cost of our IND, we do the same thing in the biology platform. We do the same thing with the clinical development, as you saw me share, you know, where we're seeing improvement in enrollment and so forth. There's so much work to be done, but this is what, quite frankly, gets us excited.

Both using whether it's a genetic agent or automations 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. And 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 are is going to be important. So that's the kind of fortitude and resilience that's needed in the space and we're adding an area to Worlds coming together and Tech and bio, haven't really come together before and not just building models that are interesting but actually applying models that unlock value. And so just to to tie it together, we are constantly looking at metrics and steps.

Neerja Khan: It is hard, but incredibly challenging and rewarding work. Thank you all for your support to our partners, you know, to our shareholders, but most importantly, to patients that are willing to take a bet on us and our programs and that are waiting. We are working as hard as possible to really, you know, forge a new era of how medicines are made for patients that are waiting. Thank you again for joining us today. We look forward to sharing more updates in the coming months.

Najat Khan: It is hard, but incredibly challenging and rewarding work. Thank you all for your support to our partners, you know, to our shareholders, but most importantly, to patients that are willing to take a bet on us and our programs and that are waiting. We are working as hard as possible to really, you know, forge a new era of how medicines are made for patients that are waiting. Thank you again for joining us today. We look forward to sharing more updates in the coming months.

The team knows, I call it green shoots, whether it is the number of compounds, we sent first, just 90% less than industry. The speed with which the cost of our imds. We do the same thing in the biology platform. We do the same thing with the clinical development as you saw me. Share, you know, 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.

You know, to our shareholders.

But most 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, you know, Forge a new era of how medicines remain for patients that are waiting.

Thank you again for joining us today, and we look forward to sharing more updates in the coming months.

Q4 2025 Recursion Pharmaceuticals Inc Earnings Call

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