Title

Presentation

Recursion Pharmaceuticals, Inc. beats earnings expectations. Reported EPS is $-0.22, expectations were $-0.3.

Najat Khan: Good morning, everyone, and thank you for joining us. Since stepping into this role, I’ve been focused on a singular question: how do we harness the full power of AI to consistently and with urgency create better medicines for patients? That requires bold ambition and a lot of focus and discipline to create value for patients and shareholders. And therefore, our approach has been deliberate. First, we’re focusing on signal over noise, generating proof and proof points across our both wholly owned programs and our partner programs with the goal to showcase where AI can truly make a difference in creating value. Second, we are continuing to evolve our platform into a repeatable, AI-driven product engine, not tech for the sake of tech, but tech that creates products of value.

And third, underpinning it all is a strong commitment to financial discipline and thoughtful capital allocation, ensuring we’re constantly being data-driven to prioritize and invest in our highest conviction opportunities to deliver durable value. Today, I’m excited to share some of our updates. We’re making meaningful progress across all these fronts, which I, along with Vicki and Ben, will share more with you today. With that, let’s dive in. But before we do, please note that today we’ll be making forward-looking statements on this call, and therefore, please refer to our SEC filings for more information. To put the progress in context, I think it’s worth briefly stepping back to how we built the foundation to enable it. Look, Recursion has been on an intentional and, in many ways, a pioneering journey.

Early on, Recursion recognized both the immense potential of AI in drug discovery as well as the reality that, unlike many other domains, the underlying foundation, whether it’s data, compute, in biology, and broadly in science, is still being built. The map is still not complete, and that fundamentally changed how you think about applying AI. So we made a deliberate choice to invest ahead of the curve, to generate and curate proprietary data, which we’ll talk about more today; to build a scaled compute infrastructure; to integrate automation; and very importantly, to develop models that are purposeful and in a true closed-loop, lab-in-a-loop system, a phrase that has become much more in vogue now, designed not just to predict, but to test, validate, and continuously learn.

That has led to a differentiated foundation, which we continue to expand and refine today. But in parallel, what’s critically important is our focus now on translating that foundation into tangible proof, advancing programs, high-quality candidates, and overall demonstrating repeatability as we evolve toward a truly product-focused AI engine. But let’s make this all much more concrete. As a result, where are we today? What are some of the facts? So first, we have established our clinical proof of concept, our first clinical proof of concept, with our REC-4881 allosteric MEK1/2 inhibitor focused on FAP. We showed significant reduction in the precancerous polyps that are a huge driver of the progressive nature of the disease, as well as showing durability, something that’s quite unique in the data we’ve shared to date.

And why is this important? These are patients that have no therapeutic solutions to date and require life-altering surgeries and have near-inevitable CRC risk. This is a great example of how we can translate AI-directed insights from our platform into true outcomes. More on the latest there shortly. But look, this is not just a single asset story at Recursion. We now have 5 wholly-owned programs, each with clear inflection points over the next 12 to 18 months, creating not just a consistent cadence of catalysts, but then also a way for us to test, learn, and also be disciplined in our areas of programs that we invest in. And we’ll share more data from one of these programs, REC-1245, our RBM39 degrader. But this momentum is not just in our wholly-owned pipeline.

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It also extends in our partnered portfolio. With over $500 million in inflows, and more importantly, I would say, 10 milestones delivered to date, I underscore that, it’s one thing to announce partnerships, but we are really focused on delivering value from these partnerships, including many of which are first in industry. This underscores a track record of delivering tangible, differentiated outcomes, and we are deeply grateful to our partners for their close collaboration in everything that we do. Underpinning all of this is, of course, our platform, an end-to-end AI-native product engine across biology, chemistry, and clinical development, powered by proprietary data and a lab-in-the-loop system, and designed for repeatability. And I’ll share some of the latest stats from our platform later in the presentation.

And then importantly, look, we have to do this with focus and discipline, extending our runway into early 2028, while reducing our operating expenses by 30% year-over-year. This is how we are moving from promise to proof. So let me walk you through how it all comes together. How do we pull this together for the ultimate goal of delivering better medicines for patients? At the foundation is an AI-native product engine that combines proprietary multimodal data, integrated wet and dry labs, purpose-built models, and scale compute. Now we hear those words a lot, but what differentiates us is not one model, it’s not one data set, it’s not one program. It’s the integration of our tools, technologies, and our teams. Look, our proprietary multimodal data has both proprietary data that we have generated in our labs, which we also integrate with public data.

We sit in the sweet spot of leveraging both. Our automated wet labs in Salt Lake City and Milton Park, Oxford, for those that are not as familiar with Milton Park, are interconnected with purpose-built AI models, and we have in-house supercompute resources to rapidly build those algorithms and learn from them. And I have to say, and it’s not just words, we truly mean it. Spanning all of this is our greatest resource, you’ve heard me say this over and over again, bilingual talent. AI researchers who appreciate the humility in making medicines and who bring a completely different take to how we can make medicines, and drug developers and drug hunters with reps under their belts that have seen what it really takes to make a drug from start to finish, and who are open-minded about unlocking the potential of AI.

Make no mistake, the culture and the talent and the integration it takes is one of the hardest things to do in this space, and I’m excited that we’ve made so much great progress there. Now all these ingredients come together in a vertically integrated AI native platform, starting first, biology. So we can simulate and understand biology much more effectively. And we really want to move away from the stats that we only — the industry only understands about 10% of biology. This is what allows us to identify novel targets. This is where we’re pushing the boundaries to really understand the root cause of disease. The next click, this really came from the integration with Exscientia, applying generative chemistry and active learning and many other approaches to design precisionly created, differentiated molecules.

This is what helps us create both first-in-class programs for those novel targets, as well as really high-value, best-in-class programs. For instance, optimizing therapeutic index for programs that have been around but haven’t fully maximized their potential to date. And third, this is something we’ve built over the last year or so, applying our data and insights to also inform a smarter, more effective, and patient-centric path to all of clinical development. Look, all of this is great to have, but we take it together to build a broad and diversified portfolio, both internally and with our close partners, with the ultimate goal of developing differentiated medicines for patients with significant unmet need. We do it faster, and we want to do it better.

But how do we do this? Our strategy remains unchanged from what you’ve heard the last time. We want to be clear, focused, disciplined, while being ambitious. First, translating proof to products. We are advancing our deep pipeline learnings, and the goal is to have revenue-generating medicines for patients. And we do it by applying a rigorous data-driven prioritization approach. so that we only invest behind the most highest confidence opportunities. Second, as you heard me say, scaling a differentiated AI-native product engine. Look, the platform is the heartbeat of so much that we do, where each prediction and experimentation allows us to compound our learning and advantage to drive repeatability in creating better products. And third is pairing that bold ambition with disciplined execution.

Rigorous capital allocation is something we think about constantly, ensuring that there’s operational focus and that our milestones are measurable to sustain that long-term value creation. You’ll hear more about that shortly. So let’s just dive in into one of our first pillars, which is our wholly-owned pipeline. Look, I’m proud to share how this strategy is beginning to translate into early signals of pipeline progress. What you see here is a broad and increasingly diverse set of programs built on 2 key areas: number one, clear rationale for differentiation, that’s coming from a platform; and second, a defined path — a rapid and defined path, I should say, to upcoming milestones and decision points. And the differentiation across these programs takes 2 forms: one, in some cases, it starts with a novel biological target or novel mechanistic insights.

You’ll see more of these coming from our discovery part of our platform; and then the other is driven by differentiated molecular design. And then the third, more recently, as we built up the clinical development AI platform, how we design, which patients do we pick, how do we design our protocols, and how do we execute in the clinic. Let me double-click some of the latest highlights from the last quarter on these slides, a period that is marked by strong and accelerating clinical momentum. So let’s start with REC-4881. This is our allosteric MEK1/2 inhibitor. As you recall, this program is rooted in a novel mechanistic insight with the potential to become the first precision therapy for FAP. As I mentioned earlier, and I can’t mention it enough, a serious and [ underserved ] condition where patients often face very limited treatment options and no medical or therapeutic options to date.

We have generated compelling proof of concept, and we’re continuing to advance the program with urgency and vigor, including we’ve already initiated FDA engagement to define a potential registrational path forward. We’re very excited to share more update on this in the second half of this year. Next, turning to REC-1245. This is our platform-derived first-in-class target and degrader with the potential to address multiple solid tumors and lymphoma. We’re excited to share today early clinical data around the safety, tolerability, and PK profile, as promised. To date, we have observed a well-tolerated profile with no dose-limiting toxicities to date. And we’re continuing to advance the program with additional data expected later this year. In a few minutes, Dr. Vicki Goodman, our Chief Medical Officer, will walk through the details — more in detail.

And finally, REC-4539. This is our LSD1 inhibitor for the potential treatment in solid tumors, including small cell lung cancer and also in AML. What differentiates this program is the underlying molecule designed with our generative platform to overcome some of the treatment-limiting on-target toxicity seen to date in earlier LSD1 inhibitors. We’ve now initiated our Phase I clinical trial and dosed our first patient with additional updates coming second half of 2027. I’ll talk more about the program, the biology, unmet need, as well as the platform insight shortly. All of the other programs remain on track. Now we’re also continuing to see strong, consistent execution across our partner pipeline, where our platform is being applied in close partnership with our esteemed partners whose deep expertise, collaborations, and capabilities, we are deeply grateful for.

And what’s emerging, I want to highlight, is 2 potential unlocks. As an example with Sanofi, the unlock is use of AI on the chemistry design side, taking difficult and diverse protein targets in immunology and oncology, using our platform and AI in partnership with Sanofi to drug these challenging — historically challenging targets. These programs are progressing towards key inflection points over the next 12 months, including a potential for development candidate, which is a big unlock in terms of potentially onboarding that asset to partners’ portfolio. And with Roche and Genentech, the unlock is on the biology side. Roche and Genentech have been pioneers in really thinking about leveraging biology perturbation at scale to really take large-scale multimodal maps and translate them into actionable and validated programs.

So the unlock here is you hear a lot around large-scale data sets being generated across the industry. Well, the unlock is how do we translate that using foundational models that we’re building and robust experimental target validation into not only validated targets, but potential first-in-class programs, something the field has long aspired to do. So we have a potential first on track in the next 12 months or so. Look we’ve talked a lot about our wholly owned programs, our partnered programs, excited about the momentum we’re building here, and about our platform that underscores it. But the secret sauce of any organization is talent. Talent is critical to everything we do, and continuing to build a strong, experienced, ambitious, and humble team is a key part of how we drive value.

And with that, I’m really pleased to introduce newest member of our executive leadership team, Dr. Vicki Goodman, our new CMO. Vicki comes to us with an incredibly strong track record of delivering transformational medicines for patients across many parts of the industry, starting at the FDA, large pharma, and biotech. You can read about all her credentials on the slide, which I won’t go through in detail. But simply put, Vicki is the right person with the right skill set to lead Recursion’s clinical development in this next chapter of the journey, but more importantly, with the right heart and perseverance to go through the trials and tribulations that’s drug discovery and development. With that, I’m going to turn it over to Vicki. Vicki, why don’t you kick us off with a few words about joining Recursion and then more details about REC-1245.

Vicki Goodman: Thank you, Najat, for the kind introduction and for the opportunity to work with you and the rest of the Recursion team. One of the reasons I joined Recursion is because the breadth and differentiation of our pipeline represents one of the most exciting opportunities to translate AI advances into meaningful therapies for patients. Today marks exactly 1 month since I joined. And even in that short time, I’ve found Recursion to be a place where scientific rigor, intellectual curiosity, and a deep spirit of innovation are brought to bear on the creation of new medicines that matter. It’s wonderful to be part of the team, and I look forward to continuing this important work. Today, I have the privilege of presenting an exciting clinical update for REC-1245 from the ongoing DAHLIA Phase I study, including preliminary safety and pharmacokinetics.

REC-1245 is an RBM39 degrader currently in Phase I for the treatment of patients with solid tumors and lymphomas. RBM39 is a novel target, which plays a central role in splicing fidelity. When RBM39 is degraded, it induces widespread splicing defects to which tumors that are already under stress, such as those with DNA damage repair deficiencies, global genomic instability, or replication stress, may be particularly sensitive. Additionally, RBM39 is highly expressed in certain tumors and is associated with disease progression and poor survival. The relevant patient population is estimated to be over 100,000 patients in the U.S. and EU5. So that’s the why RBM39 is an interesting target. The how we came to be working on RBM39 is a story we’ve touched on before.

It’s an example of how the biology element of our AI-driven platform enables the identification of novel therapeutic targets. Using genome-scale phenomic mapping, our maps of biology, RBM39 emerged as a functional analog of CDK12. This novel relationship, which came from an unbiased platform insight, was not obvious from sequence homology or traditional pathway analysis. CDK12 is a well-known oncology target for its role in DNA damage response modulation, but it has generally suffered from challenges in selectivity because of how homologous to CDK13 it is. Following our insight, we developed molecular glues and degraders for RBM39, and we showed that these phenotypically mimic CDK12 loss but not CDK13. This provides a druggable potential analog for CDK12 without the CDK13-driven toxicity.

We progressed from target ID to IND-enabling studies with roughly 200 compounds synthesized in 18 months, which is significantly faster than traditional approaches. We then needed to correlate our insights with the mechanism of action for RBM39 to translate them into clinically actionable hypotheses. We confirmed through in vitro studies that there is a greater sensitivity to REC-1245 in cell lines that have higher replication stress and DNA repair vulnerability versus cell lines that don’t have higher replication stress. And in the panel on the right, you can also see that in vivo tumor regression in an MSI-high ovarian CDX model was also demonstrated. We’ve carried these insights forward into the design of our DAHLIA Phase I/II clinical trial.

Our early clinical strategy focuses on tumor types with those same characteristics that suggested sensitivity in our preclinical experiments. The safety and PK data we are sharing today is from 16 patients enrolled across the first 4 dose levels. All patients have advanced solid tumors, and 7 of the 16 have MSI-high or mismatch repair deficient tumors. Importantly, REC-1245 is well-tolerated. Across the dose levels evaluated to date, there have been no dose-limiting toxicities reported. The most common treatment-related adverse events that have been observed are GI-related, constipation, nausea, and vomiting. As you can see, these are generally low grade with one grade 3 event of nausea and vomiting reported. There have been no treatment-related serious adverse events.

Dose escalation is ongoing and recruitment is on track. We have an early PK/PD summary from the evaluated patients to date, and we’ll have more dose escalation data and a fuller PK/PD update in the second half of the year. So far, we are seeing predictable dose-dependent exposure with exposures continuing to increase as we move through the dose levels and PK data that are supportive of daily dosing. Our initial PD data also confirm target engagement. We expect, as we move through the next 2 dose levels, to see exposures that are correlated with tumor regressions in mice. Overall, RBM39 represents an end-to-end example of how we’re using AI to translate a novel insight into a potential medicine, not just identifying a target, but building a coherent biological hypothesis that informs clinical strategy.

I look forward to sharing more data with you later this year. And with that, I’ll turn it back to Najat.

Najat Khan: Thank you, Vicki. Now moving on to LSD1, REC-4539. First, I’m pleased to share and announce that we have dosed the first patient in our Phase I clinical trial. But taking a step back, let’s discuss a little bit as to why we think that LSD1 is an interesting target for Recursion. As many of you know, LSD1 is an epigenetic regulator with a range of cellular functions and a promising oncology target across multiple cancer types. However, so far, clinically, the potential of LSD1 inhibitors have not been fully met. Previous clinical attempts to drug LSD1 have shown some efficacy, but have been limited by on-target and dose-limiting thrombocytopenia. So while the biology is understood, the challenge has been at the level of the molecule.

And therefore, we believe this has the potential to unlock a meaningful therapeutic opportunity, particularly in settings like extensive stage small cell lung cancer, where there are approximately 45,000 patients in the U.S. and EU5 with emerging but still limited treatment options after progression on first-line therapy. So for this program, the starting point model. And this is where our chemistry AI part of the platform really shines. We intentionally moved away from traditional bias chemistry, chemical space, and instead used a blank white sheet, active learning to explore a broader or information-rich space, which allowed us to identify novel starting points that wouldn’t typically be pursued. What that led us to is identifying a new scaffold.

And we iteratively refined it, ultimately arriving at the compound REC-4539, in approximately 20 months in just over 400 synthesized compounds, much faster and fewer than what is industry standard. Compare that also to what Vicki had mentioned with RBM39, 18 months and about 209 compounds synthesized to date. So you’re starting to see these, as I like to call them, green shoots in terms of the number of compounds synthesized, the speed, and also the efficiency, and how we’re generating compounds. The focus here, though, was designing a molecule with properties that directly address the limitations we’ve seen in this class to date, specifically reversibility and a shorter predicted human half-life to potentially reduce the risk of cytopenias that have been one of the dose-limiting factors for prior LSD1 inhibitors.

And we’re sharing here also some preclinical data in small cell lung cancer that demonstrate that this compound had more minimal impact on platelets while maintaining efficacy, which is not shown here, but we’ve seen that data as well, while being comparable in efficacy to other agents in the class, but having more minimal impact on platelets versus other agents in this class. In addition, there’s also another feature of this compound. It’s brain penetrant, which may be particularly relevant for patients with small cell lung cancer where up to 50% develop brain mets. This differentiation and the potential to improve tolerability has encouraged us to advance the compound into Phase I. We had our first patient dosed in April. This is a dose escalation study in select solid tumors, including small cell lung cancer, with expansion cohorts planned following the initial escalation phase.

And I want to make one thing really clear. Vicki and team are structuring these programs to enable rapid data-driven decision-making. This is how we really manage our capital allocation, specifically to address rapidly whether the emerging clinical profile really supports the hypothesis of mitigating or reducing the risk of thrombocytopenia. We expect to share initial PK and safety data in the second half of 2027. So look, I won’t — I think I’ve covered most of this, but similar to what Vicki shared, here’s another example where we use our AI platform to solve for design challenges around a biology that’s more validated, optimizing molecules where we believe there’s been limitations to date. And recall, there are no FDA-approved LSD1 inhibitors to date, despite a well-defined patient population and significant unmet need that remains for patients.

We look forward to sharing more clinical data for this program next year. Now let’s go to our second pillar, which is incredibly important. Beyond our portfolio, underpinning a lot of what we’re doing is continuing to advance our end-to-end AI product engine, pushing the boundaries. So we continue to remain at the forefront of AI-driven innovation. Let me walk you through the platform and also some of the facts and the stats around how we’re doing. We built the Recursion platform specifically to address the most persistent bottlenecks in drug discovery and development. And so we’re always looking at how we’re doing versus industry. So let’s start with biology. Look, we have generated more than 10 high-dimensional maps of disease biology. And what’s interesting here is more than half of them have been in partnership with Roche and Genentech.

And these are already driving multiple novel programs that will target programs in our internal pipeline and then we’re also working actively with our partners at Roche and Genentech to translate these maps into novel targets and first-in-class programs. This is an important unlock. Why are these maps important? Biology is a systems level approach. We need to understand the interconnected circuits. And therefore, enhancing our ability to identify and prioritize targets with better confidence, with better understanding of the underlying biology is critical to really determining the root causes. If we go to the next slide, we are also synthesizing, as you heard me share briefly, more and more compounds where we are designing 90% or synthesizing 90% fewer compounds than the industry benchmark.

So about 330 compounds on average versus 2,500 to 5,000 compounds, which is the industry standard today, while also advancing these programs to advance in development candidates roughly twice as fast. This is a meaningful step change in both efficiency and cycle time, and something that we watch very carefully on an ongoing basis. The next point are ClinTech capabilities. Where we have deployed it, we’re already seeing about 30% to 60% faster trial enrollment. This is very important for us, both for rare diseases and competitive areas such as in oncology, while increasing the eligible patient population for some of these programs from 10% to 40%. This directly impacts our time lines and speed at which we can generate high-quality clinical data.

And the underpinning it all is an integrated platform with more than 50 petabytes of proprietary multimodal data. This is incredibly critical for not just building purposeful models, but ensuring that we have our data moat that we not just invest in, but continue to expand. So suffice to say, these are not theoretical or isolated improvements. These are real, tangible gains that we keep measuring and focusing on to reinforce how our platform is changing the way we are discovering and developing our medicines. Again, I always like to say there are green shoots, but this is how we are pushing the frontier of what’s possible with our platform. Now let me double-click on a couple of recent examples in the biology layer where we’re really pushing the next generation of our models that were recently published.

Big picture, one area of focus for us in our biology platform is learning the language of biology. Sounds simple, not easy, incredibly important. And we do it across many data layers by generating perturbations at scale, whether it be genetic, chemical, and so forth. Why is that important? What is our goal? Our goal is to, a, understand biology more comprehensively; b, and then be able to predict and simulate perturbations before we are even running a single experiment. And third, that we can actually generalize beyond the data that we’ve already seen. That’s really important with these foundation models. You want to predict responses in new, out-of-distribution contexts, such as novel targets, combinations, and cell types. And why does it all matter?

Given the vastness of the biological space and how little best in industry know 10%, this has the potential to unlock areas that remain intractable today. And that becomes even more powerful when we connect different data layers, high-content cell imaging, transcriptomics, proteomics, patient data, and more, to really have a more unified view of biology. Now it’s not just in theory. We’re making progress here already. Step 1 is to actually develop a new generation of models. So let me share with you 2 recent advances in transcriptomics foundation models that we just published in the last month. The first is TxPert, which we recently published in Nature Biotech. TxPert is a model designed to predict how gene expression changes in response to different perturbations, essentially helping us to understand biology and how it will respond before we run the experiments.

So similar to what you saw in chemistry, the reason why we can reduce the number of compounds we synthesize is because we predict and simulate more, and then, of course, we make less. And how can you do that more in biology? It’s important for us to understand the systems approach in biology across different data layers and be able to predict well so that we can do less experimentation and only do the experimentation that really matters. What’s particularly exciting about this model, and listen, we’re still in early days, but it’s great to see the progress from our teams, is learning patterns in biology. It’s not just memorizing, it’s actually learning the underlying patterns. The second is generalizing beyond the data it was trained on. This is an important start.

It’s a start to predicting responses to new perturbations, new combinations, and even new cell types. Watch this area more. I think this is going to be really, really important in biology and foundation models, just given the vastness of what we’re working with. And it’s an important first step towards how we think about building a virtual cell, a term that is overused. But the importance of it is, again, can we simulate and explore biology more comprehensively, more computationally before we move into the lab? This is incredibly important so that we can be more effective and efficient and ultimately improve the probability of the targets that we could put into our programs. Next is another model, which is complementary. TxFM, our transcriptomics foundation model, which represents a significant step of actually connecting lab biology to patient biology.

I won’t go into the details here, but just want to highlight a couple of things. First, it is built on a highly curated combination of both proprietary and public data, bringing together diverse data sets into a shared representation space. Why is this important? There’s a lot of conversations, is quantity important or quality. Both matters. So you’ll see from some of the early insights here that the quality and the model architecture was really important to ensure great model performance here. So what’s exciting is the following: number one, the result is a model that surfaces much richer understanding of biology and reducing experimental noise. Batch effects and so forth, very, very important in this space; number two is it outperforms a lot of leading foundation models.

But more importantly, it outperforms models that are trained on 100x larger datasets, demonstrating that advantage I was mentioning on just the data, data curation approach, and also model architecture. And what I like is the interpretability. That’s where we’re starting to go. And again, early days. It doesn’t just rank genes, but it reveals the gene networks, the circuits, the patient subtypes from RNA data, so that transcriptomics can in time become a more systematic engine for understanding the mechanistic and target hypotheses that underpinning just one-off analysis. There is so much richness data, and we’re driving to understand that even better. And practically, again, it’s how do we get more efficient in the experiments we run? How can we do less reruns?

How can we do better cross-study comparison and efficiently use our resources? So today, both these models are starting to be deployed in our platform. For TxFM, we’re starting to leverage it for target identification, better mechanistic understanding, and patient stratification. So sharing some latest and greatest here, and we will, as always, in the months to come and years to come, share how this is truly impacting our platform. That’s what we care about. How do we take data models to really show the translation of proof into our programs, into our partner programs, and progress better medicines for patients. With that, I’m going to turn over for our third pillar, which is how do we drive all of this important work with good discipline and good ambition.

Ben?

Ben Taylor: Thank you, Najat. Our core focus from a financial perspective is ensuring we have adequate runway to achieve multiple upcoming milestones. We continued our trend of operating discipline with a 30% year-over-year reduction in cash operating expenses. We were able to achieve these savings while also growing our pipeline, partnerships, and platform by focusing only on those operations that had clear and measurable impact. In addition to our operational discipline and infrastructure simplification, we also expect ongoing efficiency gains from our technology advancements and the adoption of agents. During the quarter, we received our fifth milestone from Sanofi, advancing a potential first-in-class program for a novel biological target.

We closed the quarter with $665 million in cash and equivalents, which we believe provides operating runway through early 2028 without additional financing. For 2026, we are maintaining our cash operating expense guidance of less than $390 million, which fully funds our expected milestones and partnerships during the period. And to take you through those milestones, I’ll turn it back over to Najat.

Najat Khan: Thank you so much, Ben. And look, I’m going to close by saying we have a lot of important work ahead of us and very exciting work ahead of us. As we look ahead, we have a clear and consistent cadence of milestones, both across our wholly owned pipeline and our partner portfolio. In our wholly owned pipeline, we expect multiple clinical readouts over the next 12 to 18 months; in fact, for every single one of our clinical stage programs. Continuing to build on clinical evidence and test the hypothesis underlying our platform. In parallel, we’re seeing continued progress across our partnered portfolio. I’ll recap the 2 potential unlocks I mentioned: one, looking at the use of AI to develop novel compounds for difficult-to-drug targets.

We’re excited about our work with Sanofi here and some of the development candidate decisions coming up in the next 12 to 18 months. And with Roche and Genentech, to take all of these large, multimodal maps that’s helping us understand biology better and really translating that to novel targets and first-in-class programs. Taken together, this creates a diversified sets of catalysts and also the increasing momentum, as you’re seeing, month-over-month, week-over-week, as we look to take and harness all of what AI and our dramatically excellent team can do to turn that into meaningful outcomes. I’ll just close by saying we are focused on building an increasing body of evidence that this approach can translate, advancing differentiated programs, unlocking new biology, and doing that work with improved speed and efficiency.

And that’s what gives us confidence in the path forward. The momentum you see, the work that the teams are doing, but also the system behind it and its potential to generate outcomes over time in a repeatable fashion for patients, our partners, and our shareholders. Thank you again for your time and attention, and we will open it up now for questions.

Najat Khan: Great. So let’s dive in. I have Vicki and Ben, who will help me cover some of these questions. So the first question is from Dennis at Jefferies and Priyanka at JPM. On the REC-1245 program. Can you talk about the level of target engagement that you feel is needed to drive efficacy and where you are relative to those levels? What are common on-target safety and tolerability issues that you’re hoping to avoid with your approach? And how are you thinking about biomarkers being explored? Well, maybe I’ll just kick it off and then I’ll hand it over to Vicki to also share additional details. I’ll go in order of second, third, and first. So what are common on-target safety tolerability issues that you’re hoping to avoid?

Look, first of all, we are encouraged by the favorable safety and tolerability profile that we see to date. As you saw, 90% of what we see so far are grade 1 and 2, mostly GI, and no DLTs to date. In terms of just RBM, there are areas that we would usually keep an eye on in terms of potential tox is heme tox. And to date, we have not seen any grade 3 heme tox at all. So that is encouraging. But again, we’re in the middle of dose escalation. So more to come, as Vicki mentioned, second half of 2027. And in terms of target engagement, we’re already — we have some PD data that we shared, and Vicki can share more about that. But we’re seeing good target engagement. We’ve confirmed that to date. As we have more dose escalation, what we’ve seen preclinically is about 70% to 80% was sufficient at efficacious doses, but we’ll be tracking that as we continue further.

Vicki, did you want to add anything more to those 2 questions?

Vicki Goodman: So coming back to the safety and tolerability issues, I mean, again, what we’ve seen so far is mostly low-grade GI tox. We’ll certainly continue to monitor that as we move forward. Hematologic toxicity, which is a concern here, is something we’re really not seeing at this point. Again, we’ll continue to monitor as we continue to increase the dose, and we’ll have more data for you there in the second half of this year. Relative to target engagement, I think the estimates are spot on. I’ll add that in — we’re coming close now within the next 2 dose levels to being at the exposure levels where we saw tumor regressions in mice. So I think that’s an important point as well. Obviously, we’ll continue to monitor the target engagement in terms of RBM39 degradation and, again, have more data, more fulsome update in the second half of the year.

Najat Khan: Thank you, Vicki. And just the last question was, how are you thinking about biomarkers being explored? As Vicki mentioned during the presentation, we’re looking across select biomarkers. And, of course, as the data matures, we will look at relative benefits versus not across those biomarkers. More to come second half of 2026. Thank you for the great question. All right, next question from Gil at Needham, Alec from Bank of America, Sean from Morgan Stanley, Brendan from Cowen, and others on REC-4881. Given the encouraging Phase II data for REC-4881 in FAP and ongoing FDA engagements, what are the key uncertainties around the registrational pathway? We have 3 questions here. I’ll just start one at a time, so we can keep track.

I’ll kick it off, but Vicki, it would be great to get your thoughts. But we’re very excited about the data that we see with FAP. And with every day that goes by, we engage with more FAP patients, really not just the unmet need, but how underserved these patients are is becoming even more and more apparent. We have a significant polyp burden reduction and durability that we’ve seen to date. I would say the main areas of focus with the FDA is what would be for any asset that’s a first in disease. We have other assets in our portfolio that are best-in-class, where the regulatory approach is already very defined. For a first-in-disease, it’s really around patient population, endpoint that has clinically meaningful benefit, and then, of course, dose and dose escalation.

But those are the conversations. And as Vicki mentioned and I mentioned, we’ve already started that engagement. Anything to add?

Vicki Goodman: Yes. So I think here, because there really is a lack of regulatory precedent, it’s really important for us to work closely with the FDA in terms of defining the registrational path. To that end, we’ve already initiated that engagement within the oncology review division, also requested input from the GI division. And certainly, as we go about these, we’re thinking about leveraging the rare disease framework as well. So we can derisk this program by really closely aligning with FDA on what are clinically meaningful endpoints for patients that will help us define the primary endpoint for our pivotal study.

Najat Khan: The next question is still on REC-4881. Has there been any shift to timing for FAP regulatory? And when will we see additional data? So a couple of things. We’re on track with it. We’d initiate FDA engagement first half of 2026. We’re actually a bit ahead of schedule. We have initiated FDA engagement. And as Vicki mentioned, we expect us to be working with the FDA very closely, given it’s a first-in-disease on our potential registrational study. And then when will we see additional data? Vicki, do you want to share that? I’m happy to take it. In terms of additional data, we have already initiated 18 and over patients. We’re already recruiting those patients. And we’ll also have potentially additional data from our Phase II that we will share either here or at a forum going forward.

But we’re on track. Have you leveraged any ClinDev capabilities from your platform in assembling the proposed pivotal study design? Great question. A couple of areas. Number one, if you recall, the natural history that we did in parallel with our clinical program has been really, really important for a few reasons. This is a rare disease, limited literature, natural history. This has allowed us to not just understand patient trajectory, but also helped us as we think about how do we power the study, what endpoints are important, and so forth. So it gives us a much richer contextualization understanding, and then also incorporating that in terms of our study design as well. In addition to that, for our 18 and over plus our potential registrational study, we are also going to be using our clinical development AI capabilities for recruitment.

This is a rare disease. We want to be much more efficient and use these approaches to go to where patients are and recruit with speed and rigor. Great. Next question from [ Bruce, Philip, Rishabh ], and others on the platform. How does Recursion evaluate whether its platform is improving its ability to identify and eliminate lower-quality candidates earlier in the discovery process compared with prior years? Let me answer that one first. So as I shared earlier today, we look at every segment of our platform, and we’re really looking at how is it that we can design better molecules faster. One of the things is you asked about lower quality candidates. This is where if you can actually simulate more, predict which compounds would actually have better versus worse ADMET properties.

But this is where active learning and the multiparameter optimization, that complexity that we can do it in a very — industry can do in a very sequential way, we do it in a much more efficient way. We simulate online and only synthesize the compounds that we have confidence in. That’s where you see some of the numbers shifting pretty dramatically, cycle times becoming half and then also 90% less compounds synthesized. So that’s just one example as to how we track it. The other thing I would say on the biology side, look, the maps of biology give you a lot of hypotheses in terms of potential novel targets. But we pair that with really robust experimental validation. I think that is incredibly important to do both. That allows us to look at targets that no one has looked at before.

This is where novel biology is coming from. But we always pair that with rigorous experimentation. And that’s where the lab, the wet and dry lab, is incredibly important for us because we can do it at speed, we can learn fast, and all that data is captured to make our models better. So we are a continuous and rapid learning organization. And I will say the integration with Exscientia has really helps there, because now you have both the biology and the chemistry side sitting side by side. And we iterate and learn from that. Okay. What are the investments you’re looking to make on the platform? Compute, data generation models. Is this question from one of our leaders — AI leaders in the company? I’m just kidding. Look, we are — our strategy is we invest in our programs with our platform, and that platform needs to remain differentiated.

So we surgically invest in areas that matter. So as an example, on the clinical development side, you’ve seen the investment we have made, but there’s a reason. It’s to make a better product. So how do we recruit faster, how do we pick the right patients. In our chemistry and design platform, we’re continuously evolving and iterating on our models. And we’ll share more in due time. And then in our biology platform, you’ve seen the investments we’re making in state-of-the-art transcriptomics models. But again, they’re all with a purpose. How do we improve the target that we’re putting into our programs, the compounds that are high quality, and ensuring that we execute our programs with flawless execution clinically, but then also pick the right patients and increase our signal to noise.

Maybe I’ll take one more question. I want to take as many. I know we’re a little bit over time. From Gil and Sean on partnership strategy. Any expected guidance for a potential clinical opt-in from partners? Will we receive an update on this? So actually, Ben, do you want to share on that one?

Ben Taylor: Sure. Happy to. So as we continue to advance the programs along with Sanofi and begin to move different programs from Roche into the design phase, we absolutely expect to see some of those 5 programs that have hit their early discovery milestones move into the opt-in, and we’re working very closely with Sanofi to make that happen as quickly as possible. I think there’s also a broader point that’s really important around the partnerships. If you take a step back, and we get a lot of questions on what’s our partnership strategy, where do we plan to go in the future, if you take a step back to what we do, part of our mandate is how do you create a risk diversified model for being able to be an investor in the biotech space.

And so we obviously have transformative potential medicines that are coming up through our internal pipeline. But you also have to look at our partnership business and see how we’ve been able to advance programs and do it in a capital-efficient way and really diversify that risk and diversify what the long-term benefits of that are. So we will absolutely continue to drive that partnership forward along with our internal pipeline, and we’ll balance out how we’re getting the upfront payments while also still maintaining a lot of that downstream economics.

Najat Khan: But suffice to say, Gil and Sean, we’re working actively on this, and, of course, we’ll share updates in the next 2 months. Last question. You’ve generated over $500 million in partner-related payments to date. How should we think about the forward trajectory of platform monetization, particularly the balance between near-term milestones and retaining long-term economics and wholly-owned programs? I think it’s very similar to what Ben said. Our platform is focused on generating better products. That’s what we focus on, whether we do it internally or with partners. And we create optionality in terms of our wholly owned programs. Some of the programs, again, we’re data-driven in our approaches in terms of could be wholly owned, could be partnered, could be outlicensed, and same goes for some of our partnered programs as well.

So with that, I’ll just close by saying thank you so much for your time and attention. Thank you for all of the questions. We have a lot of momentum, a lot of important work ahead, and we continue to move that forward and excited to share more updates in the coming months and years as well. Thank you again.

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