Good morning, everyone, and thank you so much for joining us. I want to start by briefly framing where Recursion is today in its journey and evolution.

Over the past decade, Recursion has built something truly special, a differentiated platform pioneering the integration of large-scale biological data generation, machine learning, and compute to better understand the complexity of biology. We have also deliberately strengthened the foundation in chemistry and AI through the acquisitions of Exscientia, Valence, and Cyclica, creating a truly powerful foundation.

Today, we're at an important inflection point. We're harnessing everything that we've built to date to do two things: number one, translating insights into evidence, evidence that this platform, use of AI end-to-end, can generate medicines that matter.

We're doing this both across our wholly owned portfolio and through our partnerships, with strong momentum across both fronts, and we're excited to share some of the updates today. In parallel, we're also continuing to advance the platform itself.

Today, we have what I like to call a trifecta that's required to make impactful medicines: AI-driven biology, AI-enabled chemistry, and AI applied to clinical development. We continue to invest to ensure we're defining the standard for how AI is applied across the full lifecycle of R&D.

Look, as we look across the sector, we are encouraged by the broader momentum in the field: new models, new players, new partnerships being announced. The industry is clearly entering a new phase where value is being defined not only by the models you build and the collaborations that that are announced, but by actually translating those—this is the hard work—into capabilities, into real application and measurable impact.

The important question now is not only what you build, but what you can unlock. That's the chapter Recursion is in.

Our focus is on unlocking that value, using AI end-to-end consistently to generate better targets, better molecules, and advance programs faster with repeatability. The ultimate goal is to deliver medicines that matter.

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; on the partnership side, a fifth milestone with Sanofi, reflecting our growing joint portfolio tackling highly challenging targets, we're excited to share more about that today; and the continued evolution of our end-to-end AI platform; and last, but certainly nevertheless, disciplined execution, which is something we talked about at JPM, which has now extended our cash runway into early 2028.

Look, there's a lot to cover today, with that, let's jump right in. Today, we'll be making some forward-looking statements on this call, please refer to our filings for more information.

All right. We always at Recursion start with the end in mind.

In that case for us, like I said before, it's medicines that matter that are truly differentiated. In order to do that, you have to use the right data, models, compute, and more.

Look, there's a lot of talk about data, but what really matters is data that's high quality and fit for purpose. At Recursion, our foundation has been building high-quality data at scale, not just one type of datasets, but multimodal across the board.

This is where pioneering the lab-in-the-loop, pioneering pioneering the wet and dry lab has become incredibly important so that we not only generate data, but then we generate purpose-built models that we test, learn, and improve. The other thing I want to say is we sit in a sweet spot of being able to leverage both public data and our proprietary private data.

That's incredibly important to ensure that our models are impactful, insightful, and unique. 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.

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. The ultimate secret sauce, I will say, is how it all comes together: having an integrated end-to-end operating system that is a continuous learning loop all the way from novel biology or novel insights through to the clinic.

Look, for many of us that have actually made medicines and have focused on this, which is a humbling effort, we all know that improving one decision in R&D is simply not enough. It's the compounded impact of better decisions across molecule, biological insight all the way to 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, and that's what we are focused on at Recursion. What does that result in?

In our clinical development, we have a diversified portfolio. We are very encouraged by our first AI-enabled clinical proof of concept with FAP, which has the potential to be a first-in-class for FAP, but we also have additional programs behind that.

In addition to that, in our discovery portfolio, we also have another diversified set of programs. Specifically, I'll just touch on the partner piece where we have brought in over half a billion in upfront and also milestones, and we'll share some additional updates today.

I just want to say every single milestone we achieve is not just, it's improves the economics, but it's also a validation of the platform and a validation that we are learning fast in terms of what works, what doesn't to make our platform ever more intelligent. In addition to that, let's just talk a little bit about the platform.

I I'm going to share this slide every time we have an earnings because this is so core to what we do. Number one, being end-to-end, like I said before, is critical.

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

The other thing I also want to say is it's important to to innovate not just on data generation, but also your models. We have state-of-the-art, and I'll talk a little bit more about this, foundation models not just in phenomics, but transcriptomics, and pulling those together in emerging virtual cell efforts that we're also focused on.

We are also continuing to innovate on additional frontier models in the chemistry space, as well as our newly built clinical development AI platform. Again, it is that integration and how you harness it to unlock value that matters the most.

Next slide. 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, this is so important, both from our clinical portfolio as well as our partner programs; second, like I said before, in parallel, continuing to invest surgically in our platform, grounded in areas that will enable us to have more of those proof points; and third, but certainly not the least, pairing that bold ambition that we have with disciplined execution: how do we do more with less?

Let's go through each of these. If we go to the next slide, one area that's really important for us is we like to track what are our wins and learnings as we go through each of these pillars, you'll get used to seeing that as well going forward.

First, in our first pillar, which is really focused around making progress around clinical pipeline as well as our partner programs: first, FAP. This is really, really important data for a disease that has no approved therapies to date: durable and meaningful polyp burden reduction.

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

Here we just achieved our fifth milestone to date. We'll do a double click on this, but this is an example of the repeatability of our platforms, especially around using AI to develop chemistry molecules and small molecules.

Second pillar is really focused on our platform. 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. One example is, again, in our AI-enabled chemistry platform: when we look across the portfolio, we're synthesizing 90% fewer compounds than what we see in industry, 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, and we're seeing that happen.

We're doing this two times faster, instead of it taking us taking, the industry 42 months, we're seeing on average this takes us 17 months. We're going to keep pushing on this.

The other area, let's talk about biology. We talk constantly about the amount of unknown biology, and what we're trying to do is generate, and we have generated, first-in-industry maps of biology, these huge atlases where we are trying to uncover unknown biology.

This is in partnership with our great partners at Roche-Genentech, two back-to-back maps that were just accepted, and now the team is hard at work in translating those maps into novel biological programs. Our third pillar: momentum with discipline.

Look, we have a lot of things we want to do, but we have to do it with discipline and good financial stewardship: financially, of course, but also operationally. We're really excited to share that first of all, we've seen a 35% reduction in pro forma operating expenses year-over-year.

This has come from multiple areas: sharper focus on our portfolio, yes, but then also optimizing on G&A and improving our platform efficiency, which an example of it you just heard about in the last slide in terms of the number of compounds we're synthesizing, our speed, etc. The other thing that we're excited to share today is extending our runway to early 2028.

All right. Let's dive into each of these pillars a little bit more.

Starting with our wholly owned pipeline: look, when we look at the number of programs here, we have a diversified portfolio. There are different types of differentiation across each of these programs, and I'm going to categorize it in three ways: number one, their programs where it's novel biological insight from our platform; number two, their 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 where there's significant unmet need that still exists from a patient perspective.

You've seen this slide before. We always track which components of our platform are we using across our various programs.

Let's dive into a little bit more around those three categories, starting with the platform-derived novel biological insight. All right.

Two programs that exist in that category: one, FAP, 4881, REC-4881. I don't need to say again, but like the reason why there's such a significant unmet need: 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, and has 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 phenomics, the early version of the phenomics platform, to ascertain in an unbiased fashion that MEK1/2 inhibition could actually work in FAP.

We have just completed our Phase 2 study. We had a positive clean, a clinical POC, which we just shared in December.

I'll share a little bit more about that data just to recap for those who might have missed it. One of our core next steps, and we're on track, is to initiate FDA engagement on the registrational path first half of 2026.

We also have another program that has similar elements from a differentiation perspective: RBM39. RBM39, look, has pointed to be potentially important in genomically unstable cancers, and from the patient population, as you can see that, that impacts a wide patient population.

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 for many decades, but challenging to target because of the similar homology with CDK13. Right now, that program is in Phase 1 monotherapy dose escalation, and we expect to share an early Phase 1 update on safety and PK first half of 2026, later half of this year.

All right. Let's go to the next category: emerging biology, that unconquered biology, and where we can optimize program.

There we have CDK7 and ENPP1. You'll see what we're doing from a optimizing the program perspective is both on the chemistry side and also on the clinical development side.

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 have with a wide variety of patient population that are addressable given its centrality in in in oncology.

From a Recursion differentiation perspective, others have tried this target before, and one of the key challenges has been optimizing the PK/PD, optimizing that therapeutic index. That's where we have leveraged the second element of our platform, AI for chemistry, in order to optimize the molecules, especially around gut permeability.

We also are leveraging our platform in order to figure out which patient population should be going to that could potentially impact the most from CDK7 inhibition. Progress right now: we finished our Phase 1 monotherapy dose escalation, maximum dose has been selected, and we are in progress of the combination study, which is focused on ovarian cancer second-line platinum-resistant, with more data expected first half of 2027.

Again, apologies, we're working very hard at Recursion, which is why I have lost my voice, but I will try to make it through the rest of this presentation. All right.

The next program that's also in this category is focused on ENPP1. ENPP1 loss of function mutation leads to challenges with bone mineralization, thereby leading challenges in, fractures, pain, etc.

Again, another lifelong disease that starts very early in the and 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 burden patient burden in terms of injections, subcutaneous, sometimes multiple a week.

What we wanted to do is design a molecule for ENPP1, which, again, challenging target, especially in this space for hypophosphatasia, which can be suitable for chronic dosing. IND-enabling studies ongoing for this program right now, and we expect to have a go/no-go decision second half of this year on on this program.

All right. The third category: look, these are some of the some of some targets that have validated biology, but have significant unmet need that exists.

Let's take MALT1. MALT1 is validated from a target perspective in B-cell drivers, but some of the challenges really have been around limitations around tolerability.

We, again, leverage our Recursion platform to really design molecules that could design away from some of the UGT1A1 and other off-targets that have been seen, which is going to become increasingly important with combination with BTK inhibitors and others, which is what will be the ultimate efforts in this space. We have Phase 1 monotherapy dose escalation ongoing, with early Phase 1 update data, again, on safety and PK monotherapy expecting first half of '27.

Another program that has a similar theme is LSD1. LSD1 is known to be an epigenetic regulator, really trying to prevent or inhibit some of the dedifferentiation that you see in solid tumors, such as small cell lung cancer, and also AML, with some validated data seen in AML recently.

The differentiation, again, here is can we design out some of the challenges around tolerability, which has led to some DLTs and not being able to dose up high enough, such as thrombocytopenia. This, too, Phase 1 monotherapy dose escalation is in startup, and next steps is to have early Phase 1 update on safety and PK monotherapy expected second half of 2027.

Again, we expect to start to understand if some of the tolerability improvements we're trying to do, can we actually see that early on? This is our theme around early go/no-go decisions to really understand is the design playing out in the clinic.

Another program that's in preclinical and late late preclinical is our PI3K 1047 mutant-selective. PI3K10—PI3K in general is an important oncogenic mutation linked to resistance and relapse, etc.

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 100%—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. That's an IND-enabling study.

Again, go/no-go decision second half of this year expected before we consider a Phase 1 initiation. I know that was a round the trip around our portfolio, but I would love to actually double-click on one of our later-stage, which is REC-4881, and then also one of our earlier-stage and potentially entering our clinical pipeline, which is our PI3K program.

Let's go through the REC-4881. I'm just going to do a quick update here.

For this program, we had our clinical POC late, late last year. A couple of things to note: no approved therapies.

What we saw in our Phase 2, three months on treatment with 4 mg QD of this MEK1/2 inhibitor, significant polyp burden reduction, about 43% median, highest one of the higher polyp burden reductions to date; 75% of the patients responded. In terms of the AEs that we see, very much in line with what you see for MEK1/2 inhibitors: majority were grade 1/2, rash, CPK, and no grade 4/5 to date.

What we also saw, which was even more encouraging, was when these patients were then off treatment for three months—and remember, this is a chronic disease, 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 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. This is a really important, when I said at the top of the call, like it's important to not just have insights, but how do you turn those into something that's meaningful for patients and then ultimately new medicines?

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 FDA engagement and initiating that first half of 2026 to really discuss the registrational study design.

In addition to that, we have already started the enrollment of 18 and over cohort. As you remember, some of the data we shared was for 55 and over, we're already progressing on the 18 and over, and then also advancing dose optimization efforts, really inspired by what we saw with the durability data that I shared on the last slide.

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 1047 mutant-selective. Look, for PI3K, I'm sure you're thinking, Najat, there are multiple PI3K, why are we working on PI3K?

This is a very, very important target across multiple solid tumors. The current PI3K inhibitors have been constrained, and we have some data that we'll share shortly: hyperglycemia, metabolic toxicity, dose interruptions, dose reductions, limited treatment duration.

All of that means is there an opportunity to do better by patients? There's an unmet need that still exists.

What is our differentiation, and what is our thesis, is really focusing on the 1047 mutant-selective, which has 100x more selectivity over wild type, thereby having the potential to minimize risk for AEs. In order to do that, we designed a molecule that can allow us to have that exquisite 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.

That led us to leveraging our MD simulations, and this is where compute becomes really important. Our molecular dynamic simulations revealed a novel pocket.

We then used our generative 3D modeling efforts and machine learning in order to design molecules, novel scaffold scaffolds for this novel pocket. We were able to use other approaches, our other ML approaches to really rapidly design our cycles, you get exquisite potency, but then 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. This is what gets us excited: data that we can actually do things better, faster.

Then then the next question is, how does this molecule do? We'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. If you look at the left-hand side over here, what we what you're looking at here is a dose-dependent tumor regression for our compound, as you can 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.

Given the standard of care, we also wanted to see the performance versus standard of care. With SERDs, with CDK4/6 inhibitors, which is that combo is 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 capivasertib.

We saw improved tumor regression with low dose of our asset versus high dose capivasertib. All in all, this is encouraging from a efficacy perspective for this compound.

Then we also wanted to look at tolerability. 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 matter 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. Then if you go to the right side, a little bit complicated, but we like to share data, also in obese diabetic rats, you don't see hyperglycemia or the metabolic liability even at supra-efficacious dose for our asset versus Scorpion and and Piqray as well.

Again, taken together, this is encouraging. Like I always say, the rubber hits the road in the clinic.

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% experience hyperglycemia.

It's large percent actually also have dose interruptions, dose reductions, sometimes driven by the hyperglycemia they're experiencing. 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, and you see the discontinuation about 3 to 6 months.

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 twofold: 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 and not having impact holds, and that's about 50/50% in breast cancer; and then the other is there's also broader patient populations, such as colorectal and endometrial, we can also explore.

One thing that I'd be interested to also look at is, can this patients, because of the better tolerability, stay on longer, longer treatment duration to really maximize the impact of these therapies? Again, clinical validation and improved tolerability requires is critical to confirm this expansion thesis.

If you go to the next slide, more to come. 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 is the go/no-go decision for Phase 1, which would be second half of this year.

Currently the study is in IND-enabling. All right.

That was just our first pillar double-click. We'll also do a little bit more around our partnerships.

I'm really excited to share the progress we are making, because remember, proof points can come from both your internal portfolio, which is what we just focused on, but then also from our amazing partners that we're working with on actual programs. To date, we have already achieved over $500 million in total cash inflows from our partnership, both upfronts and milestones, and we've actually laid out some of those recent ones with the momentum that then we've been achieving recently.

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. 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. Sanofi has been a fabulous, fabulous partner.

We learned so much from that exceptional team, both across I&I 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. It's focused on challenging targets in I&I and oncology, with molecules that have the potential to be first-in-class and/or best-in-class, with programs that address very specific unmet needs, thinking with the clinic, the end in mind.

To date, we have advanced five lead packages that has been delivered by Recursion across five of these programs and accepted by accepted by Sanofi to date. That's about $34 million in milestones to date, in addition to the $100 million in upfront, $134 million so far.

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. Look, discovery is probabilistic.

We know some will work and some of these programs won't, but it is the repeatability and the ability for our platform to have multiple shots on goal, that's incredibly critical for us. That's what you see with our internal portfolio, that's what you see as we work humbly with our partners to also advance important programs for patients in areas that are challenging.

Just double-clicking on one of these, how do we get there? Remember, these are challenging targets, and we are leveraging our platform.

I just want to explain one aspect that I think is really important. Our platform is not about one data, one model, one asset.

It's about the confluence of a suite of that you use for the problem at hand. 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 milestones, our fifth 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 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.

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

I mentioned about the proprietary data that Recursion has been building for a decade: 50 over 50 petabytes of high-quality multimodal data. I want to emphasize the multimodal piece.

Biology is complex, and having diversity of data and, having at-scale data sets complete to the extent possible, whole genome knockout, overexpression, that's the kind of model data that you need to then build foundation models that are state-of-the-art. We have a fantastic team that's working on this, whether it's in the phenomics foundation models or the transcriptomic is 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 inputs to outputs: genetics, transcriptomic, proteomic, phenomic, patient data.

That's the effort that we're focused on, and how do we leverage it, because that's the so what is what matters, is creating these novel proprietary data sets, we call them biology maps. We have those internally across different therapeutic areas.

We also have in neuroscience and GI onc with Roche-Genentech, and that's with 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. We have used our in silico approaches to generate over 100 million molecules.

One emphasis one point I want to emphasize is the point around synthetically aware design. There'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, that 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, scored, prioritized by our plat—by our models. One thing that we're doing increasingly, not just leveraging automation, but also agentic orchestration, we can get things done better, faster, and in a more unbiased approach.

I mentioned the stat before, but I can't wait to mention it again: but we we on average across the portfolio, with PI3K you saw 242 compounds, 10 months, but across the portfolio, we like to be transparent around our data, 330 compounds is what we synthesize on average versus 2,500, 5,000 in industry, and we do it in 17 months on average versus 40 months plus for industry. This is these are the kinds of things that we track, and that's going from target all the way to advanced candidate.

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 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, where we have done first, and again, just like we did with our biology platform and chemistry, you got to build a really good data foundation: 300 million-plus real-world lives, that's, through both some internal work, but then also the great ecosystem integrated data partnerships.

We're very opportunistic around that. Some of the early results, you can read the bullets here, but the one that I would point the attention to is enrollment rates.

Look, we are to in order to execute on programs, you have to enroll in a very efficient and intelligent way. Some of our early results for some of the programs, we're starting to see 1.3 to 1.6x improvement.

We're also just improving the operational piece that goes underneath it in terms of just starting studies faster by up to 3 months. All of this accumulates.

Remember the point around the compounding impact of decisions across the platform. This is how you do, this is how you define drug discovery and development leveraging AI.

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.

Our platform can actually generate a heat map, just like you've seen for biology or chemistry in different ways, but here for potential patients across, and we're showing the US here, across the country. Then we can go to 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 for which program, ovarian cancer's 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. Then beyond that, we can also get what how many patients do these sites have, and then you can do a filter in terms of your inclusion/exclusion and what's relevant for the type of patient that we are looking for in a specific study.

That filter does not get happen enough, I can tell you, in traditional approaches. I call this—we we talk about precision medicine, precision biology, precision chemistry—this is precision operations, and starting with the patient in mind.

With that, thank you for being with me for some time. I want to now hand it over to Ben Taylor, our CFO, to actually go through some of our financials.

Thanks, Najat. So, 2025 was a year of financial transformation for the company.

As a part of the integration, we decided to rebuild all of our corporate systems from the ground up. This was really important because we wanted to be able to apply the same level of discipline and rigor to our strategic decision-making that we do to all of our scientific decision-making.

We looked at how every dollar 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.

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. That's how we were able to come back and have a 35% year-over-year reduction from pro forma '24 into '25, and even come in 10% below the guidance that we originally provided in May of last year.

We ended the year with $754 million in cash. Looking forward, our 2026 cash operating expenses are expected to be under $390 million.

Cash operating expenses is a non-GAAP measure that we're going to be using to give you guidance. We have a lot of non-cash expenses in our P&L, and so 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. 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. With that, I will hand it back over to Najat.

Thank you so much, Ben. We'll wrap it up by just saying, looking ahead, we have a very broad set of catalysts that are coming up, and it's going to be a busy a busy next 18 to 24 months.

We'll see if I can recover my voice soon. In terms of this year, like I said, we're on track for our initial engagement with the FDA on REC-4881, we're looking forward to that, and also initial data, early safety and and then also PK for RBM39, and go/no-go decisions for PI3K and ENPP1, which are both in IND-enabling.

We'll also have additional data for 4881 early next year, and then combo data expected for our CDK7 program, as well as more early safety and PK data for MALT1 and LSD1. Recall, for both of those, we designed the 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. Our partnerships with Sanofi, as we just discussed, in terms of multiple programs as we're progressing into more later-stage development candidate and other milestones as well.

In addition to that, these maps, these maps where novel biology is really come would come from, extracting that into new programs with Roche-Genentech, etc. Really, really important work that that continues.

We continue to invest and push the boundaries in terms of our platform, defining what industry standard really looks like for making medicines using AI. As Ben just mentioned, pairing all of that important work with disciplined execution.

We've really pivoted towards an outcomes-based budget where we test what every dollar, what value creation every dollar can drive, doing more with less. I'll close by saying thank you so much for the time.

Also, our focus will always remain on value creation for patients, they're the ones that we ultimately serve, patients who are waiting, and also for, of course, our shareholders. Thank you again for listening.

With that, I'm going to pivot to the Q&A section, and I'll also have our CSO, Dave Hallett, joining us as well, in addition to Ben Taylor, in order to address some of the questions. All right.

From Sean at Morgan Stanley and Priyanka as well, thank you for the questions from JPMorgan, and from Brandon at Cowen, so many people. Questions around REC-4881: understanding what potential registrational pathway may look like upon alignment with the FDA, how we're thinking about providing a regulatory update and updated patient population?

Long question, I'll break it into pieces. In terms of the regulatory update, as I mentioned, for us, we're on track for that engagement, initial engagement with the FDA first half of 2026.

All hands on deck for that. That's going to be really important in terms of discussing 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. In addition to that, I didn't cover it today in the interest of time, we also have the natural history data as well.

Coupled with that, it's going to 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 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 2027. Look, as we have meaningful updates across both fronts, as you have seen, we've done webinars ad hoc, we like to be real-time and transparent when we have more meaningful outcomes, and updates will absolutely be shared with the street as well.

All right. Next question is from Alex from Bank Alex from Bank of America: It looks like the cost-cutting measures, cost optimization, cost-cutting measures really started to take hold in Q4.

Any one-offs that helped in the quarter, or are these levels sort of the expectation for the go-forward? Ben, do you want to take that?

Sure, happy to. Thanks, Alex.

If you think about it, I agree with Najat, 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 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.

When you come in with that attitude, all of a sudden you start to find ways to do more with less, and that's where we expect to be able to continue growing our pipeline, investing heavily behind our platform, and moving things forward, while still hitting those cost targets that we put out there.

Great. Thank you, Ben.

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 always evolves 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?

etc., etc. Okay.

Great question, thank you so much. It's going to be important to decouple two parts: one is the investment from NVIDIA, and one is our technical collaboration with NVIDIA.

The technical collaboration with NVIDIA continues. 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, Recursion being a pioneer in how to leverage automation, this wet and dry lab.

This is not just words, this is actually in action. This is how we do millions of experiments a week.

The other piece is also our collaboration with NVIDIA around our BioHive-2, one of the fastest supercomputer in life sciences. That, I mentioned the examples around PI3K, around Sanofi, using machine learning, using molecular dynamics.

All of that is underpinned by our supercomputer. Our partnership with NVIDIA couldn't be any stronger, so that continues.

In terms of the divestment, this really was, if you look at the public 13 F filings from Q4 of 2025, is really a shift in NVIDIA's investment portfolio to more larger on-strategy supercomputer, data center, etc., 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, large $1 billion-plus investments. Those are to be two areas to be decoupled.

The last thing I'll also say, you also ask the question, are we seeking other technology partners? We have a strategic partnership with Google as well in terms of cloud compute.

We have the partnership, as I mentioned, with NVIDIA on machine learning and models, etc., but also on-prem compute. We will continue, we've always been one of the pioneers in really bridging the world of tech and science, and we'll continue to do that.

All right. We'll take one more question here.

From George is, 'With the recent positive preliminary efficacy data for REC-4881 in FAP and the achievement of your fifth milestone with Sanofi, what specific metrics or historical comparison from your current clinical portfolio best demonstrate that Recursion is improving the probability of clinical success or speed of development compared to traditional discovery methods?' I'm going to hand it over to Dave Hallett to get us started, and I'm sure we can also add some more comments as well there.

Thank you, Najat, and 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 as Najat during the last presentation has highlighted a number of themes. One is about the repeatability of kind of delivery.

I think that was specifically highlighted in the burgeoning Sanofi kind of pipeline 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 JPMorgan again, this presentation, I think we've highlighted the speed of delivery. If you look at the metrics that we're delivering in terms of numbers of novel compounds that we synthesize and test, and the speed that we're getting to these development candidates, these are, I think, further demonstration of the role kind of technology plays in kind of accelerating that delivery.

The proof is ultimately in the clinic, and clearly we're very excited for patients in terms of FAP. I think this is the first example from our platform where we've been able to kind of demonstrate that a compound that came from Recursion has shown clinical proof of concept.

Obviously the goal over the coming months and years is to show repeatability in that frame as well.

Thank you, Dave, and just to maybe add a little bit of a broader perspective: looking at Recursion, 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. 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, and 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, making all of this much more engineering-focused using whether it's agentic 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—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, we know that multiple shots on goals is going to be important. That's the kind of fortitude and resilience that's needed in this space.

Two 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. 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, which is 90% less than industry, the cost of our INDs. We do the same thing in the biology platform, we do the same thing with the clinical development as you saw me share, where we're seeing improvement in enrollment and so forth.

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.

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, and we are working as hard as possible to really forge a new era of how medicines were made for patients that are waiting. Thank you again for joining us today, and we look forward to sharing more updates in the coming months.