Recursion (RXRX) Q4 2025 Earnings Call Transcript

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DATE

Wednesday, Feb. 25, 2026 at 8 a.m. ET

CALL PARTICIPANTS

• Chief Executive Officer — Najat Khan
• Chief Financial Officer — Ben Taylor
• Chief Scientific Officer — Dave Hallett

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TAKEAWAYS

• Partnership and Milestone Revenue -- Over $500 million in cumulative partnership cash inflows, including $134 million to date from Sanofi (composed of $100 million upfront and $34 million in milestones).
• Operating Expense Reduction -- 35% year-over-year drop in pro forma operating expenses attributed to focused portfolio selection, G&A optimization, and improved platform efficiency.
• Cash Position -- $754 million in cash at period end, with projected operating expenses (non-GAAP cash basis) under $390 million for the coming year.
• Cash Runway Guidance -- Runway now expected to extend into early 2028, reflecting both cost discipline and partner payments.
• Clinical Proof of Concept -- REC-4881 for FAP achieved positive Phase 2 proof of concept; median polyp burden reduction reached 43%, with 75% of patients showing response and durable effects observed off treatment.
• Drug Discovery Platform Metrics -- Recursion Pharmaceuticals (NASDAQ:RXRX) synthesized an average of 330 compounds per development candidate, 90% fewer than the industry norm of 2,500, and moved candidates to advanced stage in 17 months versus the industry’s typical 40+ months.
• Sanofi Collaboration -- Five lead packages delivered and accepted, with joint portfolio targeting I&I and oncology indications and potential for each program exceeding $300 million in milestones plus tiered, up-to-double-digit royalties.
• Data Infrastructure Scale -- Proprietary datasets exceed 50 petabytes of high-quality multimodal data supporting foundational models in phenomics and transcriptomics.
• Pipeline Diversity -- Five clinical-stage programs (including REC-4881, RBM39, CDK7, ENPP1, MALT1, LSD1) with multiple additional candidates advancing through preclinical and IND-enabling phases.
• AI-Enabled Clinical Operations -- Enrollment rates improved 1.3x to 1.6x versus prior methods, and study starts accelerated by up to three months, as enabled by the company’s clinical AI platform covering data from 300 million patient lives.
• Upcoming Milestones -- Phase 1 data updates for RBM39, MALT1, and LSD1, as well as go/no-go IND decisions for PI3K H1047 mutant-selective and ENPP1, are anticipated in the next 12-24 months.
• Technology Partnerships -- Nvidia (NASDAQ:NVDA) remains a technical partner on supercomputing and model training; Alphabet (NASDAQ:GOOGL) is a partner for cloud compute, with no planned changes to these core relationships despite Nvidia's recent portfolio realignment.

SUMMARY

Recursion Pharmaceuticals (NASDAQ:RXRX) highlighted its transition to an outcomes-based budgeting approach, explicitly tying spending to quantifiable return and ongoing pipeline advancement. Management stated that non-GAAP cash operating expenses for 2026 are projected to remain below $390 million, with extended cash runway guidance to early 2028 integrating projected milestone inflows. The company emphasized that platform efficiency could continue to increase as additional discovery and partnership milestones are realized and more clinical data is generated. Collaboration milestones with Sanofi (NASDAQ:SNY) and progress on AI-driven drug discovery set the stage for substantial late-stage candidate pipeline advances over the next two years.

• Company leadership described the business as moving beyond model building to focus on unlocking value through tangible clinical and partnership deliverables.
• Management stated, "it is also a validation of the platform and a validation that we are learning fast," pointing to cumulative platform improvement as a key pillar of the investment case.
• Multiple programs aim for first-in-class or best-in-class molecule optimization using proprietary data and rapid in silico design cycles, which the team claimed is shortening the timeline to clinical proof.
• Clinical pipeline differentiation includes targets selected for high unmet need, proprietary biological insight, or unique optimization for tolerability and oral dosing.

INDUSTRY GLOSSARY

• FAP: Familial adenomatous polyposis, a genetic disorder causing large numbers of precancerous colon polyps with high risk for colorectal cancer.
• Phenomics: Study and model-based analysis of organism-wide phenotypes at large scale, often using high-content data and imaging in drug discovery.
• IND-Enabling: Preclinical studies and development work required to support the filing of an Investigational New Drug (IND) application for first-in-human clinical trials.
• PK: Pharmacokinetics, the study of how a drug is absorbed, distributed, metabolized, and excreted by the body.
• DLT: Dose-Limiting Toxicity, an adverse effect of a drug which prevents an increase in dose or level of that drug.

Full Conference Call Transcript

Najat Khan: Good morning, and thank you so much for joining us. I want to start by briefly framing where Recursion Pharmaceuticals, Inc. is today in its journey and evolution. Over the past decade, Recursion Pharmaceuticals, Inc. 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 Excientia, Valens, and Cyclica, creating a truly powerful foundation. Today, we are at an important inflection point. We are harnessing everything that we have built to date to do two things.

Number one, translating insights into evidence—evidence that this platform, the use of AI end to end, can generate medicines that matter—and we are doing this both across our wholly owned portfolio and through our partnerships. With strong momentum across both fronts, I am excited to share some of the updates today. In parallel, we are also continuing to advance. Today, we have what I like to call a trifecta that is required to make impactful medicines: AI-driven biology, AI-enabled chemistry, and AI applied to clinical development. We continue to invest to ensure we are defining the standard for how AI is applied across the full lifecycle of R&D.

As we look across the sector, we are encouraged by the broader momentum in the field: new models, flares, and partnerships being announced. But the industry is clearly entering a new phase where value is being defined not only by the models you build and the collaborations that are announced, but by actually translating those into capabilities, into real application, and measurable impact. The important question now is not only what you build, but what you can unlock, and that is the chapter Recursion Pharmaceuticals, Inc. is in. Our focus is on unlocking that value, using AI end to end consistently to generate better targets and better molecules, and to advance programs faster with repeatability.

The ultimate goal is to deliver medicines that matter. This quarter reflects that focus. We are making progress across all fronts. First, on the clinical side with a 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 are excited to share more about that today and the continued evolution of our end-to-end AI platform. Last but certainly not least, disciplined execution, which is something we talked about at JPM, has now extended our cash runway into early 2028. There is a lot to cover today, so with that, let us jump right in.

Today, we will be making some forward-looking statements on this call, so please refer to our filings for more information. We always at Recursion Pharmaceuticals, Inc. start with the end in mind, and in that case for us, like I said before, it is medicines that matter, that are truly differentiated. But in order to do that, you have to use the right data, models, compute, and more. There is a lot of talk about data, but what really matters is data that is high quality and fit for purpose. At Recursion Pharmaceuticals, Inc., our foundation has been building high-quality data at scale, not just one type of dataset, but multimodal across the board.

This is where pioneering the lab-in-the-loop, pioneering the wet and dry lab, has become incredibly important so that we not only generate data but then we generate purpose-built models that we test, learn, and improve. We sit in a sweet spot of being able to leverage both public data and our proprietary private data. That is incredibly important to ensure that our models are impactful, insightful, and unique. On top of that, I have mentioned 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 is also important to have reps under your belt, to know what good looks like. Having talented teams that have reps is one of our core differentiators. The ultimate secret sauce 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. For many of us that have actually made medicines, which is a humbling effort, we all know that improving one decision in R&D is simply not enough. It is the compounded impact of better decisions across molecule and biological insight all the way through the clinic that makes the difference.

That is how you truly change not just the outcome, but also the time and cost and how you do things, and that is what we are focused on at Recursion Pharmaceuticals, Inc. So what does that result in? First of all, in our clinical development, we have a diversified portfolio. We are very encouraged by our first AI-enabled clinical proof of concept with FAP, which has the potential to be first-in-class for FAP, but we also have additional programs behind that. In our discovery portfolio, we also have another diversified set of programs. I will just touch on the partner piece where we have brought in over $500 million in upfront and milestones.

We will share some additional updates today. Every single milestone we achieve not only improves the economics, but it is also a validation of the platform and a validation that we are learning fast in terms of what works and what does not to make our platform ever more intelligent. Let us talk a little bit about the platform. I am going to share the slide every time we have an earnings call 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 and chemistry to ultimately the patient, which is really where the rubber hits the road. That is where we are going.

It is important to innovate not just on data generation, but also on your models. We have state-of-the-art foundation models not just in phenomics, but transcriptomics, and we are pulling those together in emerging virtual cell efforts that we are also focused on. We are 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. In terms of our strategic pillars, we have three main areas that we are 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, in parallel, continuing to invest surgically in our platform grounded in areas that will enable us to have more of those proof points. Third, pairing that bold ambition that we have with disciplined execution—how do we do more with less? One area that is really important for us is we like to track our wins and learnings as we go through each of these pillars. You will get used to seeing that going forward.

In our first pillar, which is focused on making progress around the clinical pipeline as well as our partner programs: FAP is really important data for a disease that has no approved therapies to date, showing durable and meaningful polyp burden reduction. We will highlight our Sanofi collaboration. As a reminder, this is where we are tackling challenging targets in I&I and oncology and leveraging our AI and chemistry components of our platform to design novel compounds. We just achieved our fifth milestone to date. This is an example of the repeatability of our platform using AI to develop small molecules. The second pillar is focused on our platform.

As we look across the portfolio, we look at green shoots—proof points where we are seeing that we can do things better and faster. One example is our AI-enabled chemistry platform. When we look across the portfolio, we are synthesizing 90% fewer compounds than what we see in industry—about 300 versus 2,500 compounds—because we are predicting more and making less. This is where in silico approaches should be guiding us, and we are seeing that happen. We are doing this two times faster. Instead of taking the industry 42 months on average, it takes us 17 months. We will keep pushing on this.

In biology, we are generating first-in-industry maps of biology, these huge atlases where we are uncovering unknown biology. This is in partnership with Roche and Genentech—two back-to-back maps were just accepted—and now the team is hard at work translating those maps into novel biological programs. In our third pillar, momentum with discipline, 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 are excited to share that we have seen a 35% reduction in pro forma operating expenses year over year.

This comes from multiple areas: sharper focus on our portfolio, optimizing our G&A, and improving our platform efficiency, such as the reduction in synthesized compounds and our speed. We are also excited to share the extension of our runway to early 2028. Let us dive into each of these pillars a little bit more, starting with our wholly owned pipeline. We have a diversified portfolio with different types of differentiation across each of these programs. I will categorize it in three ways. Number one, programs with novel biological insight from our platform. Number two, programs with emerging biology—interesting biology that is unconquered, not validated yet—where we have developed optimized programs.

Number three, areas with validated biology but significant unmet need still exists from a patient perspective. We always track which components of our platform we are using across our various programs. Starting with platform-derived novel biological insight, we have two programs in that category. One is FAP: REC-4881. There is significant unmet need with nothing approved for these patients. This is a disease hallmarked by hundreds of polyps, each of which is precancerous, and has a 100% risk of colorectal cancer by the time you are 40. There are more than 50,000 addressable patients in the US and EU.

The Recursion Pharmaceuticals, Inc. differentiation is using the phenomics platform to ascertain in an unbiased fashion that MEK12 inhibition could work in FAP. We have completed our Phase 2 study with a positive clinical POC, which we shared in December. Our next steps are on track to initiate FDA engagement on the registration path in 2026. We also have another program with similar elements: RBM39. RBM39 appears to be potentially important in genomically unstable cancers, impacting a wide patient population.

The differentiation for Recursion Pharmaceuticals, Inc. came from uncovering this MOA and the connection it has to CDK12, which is known to be important for DDR modulation, and it has been 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 in 2026. Moving to emerging biology that is unconquered, where we can optimize programs, we have CDK7 and ENPP1. For CDK7, it is an important central master regulator of both cell cycle control and transcription, with a wide variety of addressable patient populations given its centrality in oncology.

Others have tried this target before, and one key challenge has been optimizing the PK/PD and therapeutic index. We leveraged our AI chemistry to optimize molecules, especially around gut permeability. We are also leveraging our platform to figure out which patient populations could potentially benefit the most from CDK7 inhibition. We finished our Phase 1 monotherapy dose escalation, maximum dose has been selected, and we are in progress on the combination study focused on ovarian cancer, second-line platinum-resistant, with more data expected in 2027. The next program is ENPP1. ENPP1 loss of function mutations lead to challenges with bone mineralization, leading to fractures and pain. Another lifelong disease that starts very early in the patient’s life.

The Recursion Pharmaceuticals, Inc. differentiation here is focusing on a molecule that can be oral, because what is available today for patients and some investigational agents is enzyme replacement therapy that requires a huge patient burden in terms of subcutaneous injections, sometimes multiple times a week. We wanted to design an oral molecule for ENPP1 suitable for chronic dosing, especially in hyperphosphatasia. IND-enabling studies are ongoing. We expect to have a go/no-go decision in the second half of this year. In the third category—validated biology with significant unmet need—we have MALT1. MALT1 is validated in B-cell drivers, but challenges have been around tolerability.

We leveraged the Recursion Pharmaceuticals, Inc. platform to design molecules that avoid some of the UGT1A1 and other targets that have been seen, which will become increasingly important with combinations with BTK inhibitors, which will be the ultimate efforts in this space. We have Phase 1 monotherapy dose escalation ongoing, with an early Phase 1 update on safety and PK in the first half of 2027. Another program with a similar theme is LSD1, an epigenetic regulator, aiming to address differentiation in solid tumors such as small cell lung cancer and AML, with some validated data seen in AML recently.

The differentiation here is designing out challenges around tolerability, which has led to some DLTs and not being able to dose high enough, such as thrombocytopenia. This Phase 1 monotherapy dose escalation is in startup, with an early Phase 1 update on safety and PK expected in 2027. Another program in late preclinical is our PI3K H1047 mutant-selective. PI3K is an important oncogenic mutation linked to resistance and relapse. We designed a molecule that would be much more selective—over 100x selectivity—over wild-type PI3K, which leads to some of the tolerability challenges that lead to dose interruptions and reductions.

That program is in IND-enabling study, with a go/no-go decision expected in the second half of this year before we consider a Phase 1 initiation. I would like to double click on two programs: REC-481, and our PI3K program. For REC-481, we had our clinical POC late last year. There are no approved therapies. In our Phase 2, three months on treatment with 4 mg QD of this MEK12 inhibitor, we saw significant polyp burden reduction at 43% median, with 75% of patients responding, among the higher polyp burden reductions to date. AEs were in line with MEK12 inhibitors, majority Grade 1–2: rash, CPK, and no Grade 4 or 5 to date.

When patients were off treatment for three months, we saw continued durable polyp burden reduction, in some cases deepening, with a significant amount of patients responding. We are on track to initiate FDA engagement in 2026 to discuss the registrational study design. We have started enrollment of the 18-and-over cohort, and are advancing dose optimization efforts inspired by the durability data. We expect additional clinical data in 2027. For the PI3K H1047 mutant-selective program, PI3K is a very important target across multiple solid tumors. Current PI3K inhibitors have constraints: hyperglycemia, metabolic toxicity, dose interruptions, dose reductions, and limited treatment duration. Our differentiation is focusing on the H1047 mutant-selective with 100x more selectivity over wild-type, minimizing risk for AEs.

We designed a molecule to allow exquisite selectivity. We started with X-ray structures with proprietary structural insight and leveraged molecular dynamics simulations to reveal a novel pocket. We used generative 3D modeling and machine learning to design novel scaffolds for this novel pocket and rapidly designed cycles for both potency and selectivity. We designed 242 compounds, 13 cycles in 10 months. Compared to industry standards, this is fast. Preclinically, we saw dose-dependent tumor regression for our compound, comparable to Scorpion’s and better than Piqray. We also evaluated performance versus standard of care, such as CDK4/6 inhibitors, and saw synergy. We have additional data showing improved tumor regression with low dose of our asset versus high dose capivasertib.

On tolerability, in naïve wild-type mice we did not see impacts on hyperglycemia markers versus Scorpion and Piqray, and in obese diabetic rats we did not see hyperglycemia or metabolic liability even at supra-efficacious dose for our asset versus Scorpion and Piqray. Current PI3K inhibitors in HR-positive breast cancer show 65%–85% experience hyperglycemia. We are interested to see whether, with better tolerability, patients can stay on longer. Clinical validation is critical to confirm this expansion thesis. The next step is the go/no-go decision for Phase 1 in the second half of this year. That was our first pillar. We are also making progress with our partners.

To date, we have achieved over $500 million in total cash inflows from our partnerships, both upfronts and milestones. Each program has the potential for over $300 million in milestones and tiered royalties per small molecule program, with some royalties up to double digits. We are excited to unveil our joint portfolio with Sanofi. Sanofi has been a fabulous partner. We are working on multiple programs—five—with multiple early discovery programs as well. This is a diversified pipeline 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, addressing very specific unmet needs.

To date, we advanced five lead packages delivered by Recursion Pharmaceuticals, Inc. across five programs and accepted by Sanofi, reflecting about $34 million in milestones to date, in addition to $100 million in upfronts—$134 million so far. We have important work ahead with later-stage discovery milestones over the next 18 months. Discovery is probabilistic; some will work and some will not. The repeatability and ability for our platform to have multiple shots on goal is critical. Double-clicking on one of these, our latest oncology program where we just got a milestone focuses on data-poor targets.

We leverage both physics-based approaches and machine learning—physics-based to understand protein flexibility and novel pockets, then machine learning to drive the design-make-test cycle, and find highly potent molecules now progressing to the next stage. None of this can happen without a unique and differentiated platform that is an ever-important work in progress. A snapshot of the three components of our platform: biology insight, chemistry, and clinical development AI. On biology, we have over 50 petabytes of high-quality multimodal data. Having diverse and complete datasets—whole-genome knockout, overexpression—is the kind of data you need to build state-of-the-art foundation models.

We have phenomics and transcriptomic foundation models, and we are combining those; connecting genetics, transcriptomics, proteomics, phenomics, and patient data is the effort we are focused on. We create novel proprietary datasets—biology maps—internally and with Roche and Genentech, fueling our discovery pipeline. On AI for chemistry, novel small molecules are harder than they look. We have used in silico approaches to generate over 100 million molecules. Synthetically aware design is critical; if you cannot make them or CMC is very challenging, that limits you. We always start with the target product profile. Ninety percent of molecules are generated, scored, and prioritized by our models.

We are increasingly leveraging automation and agentic orchestration to get things done better, faster, and more unbiased. Across the portfolio, we synthesize on average 330 compounds versus 2,500 in industry, and we do it in 17 months on average versus 40+ months in industry, from target to advanced candidate. As a result, we have over 10 development candidates across our internal portfolio. On clinical development AI, we built a strong data foundation with over 300 million real-world lives through internal work and partnerships. Early results include improvements in enrollment rates of 1.3x to 1.6x and starting studies faster by up to three months. This compounds across the platform. On the enrollment front, we start with the 300 million patient lives.

Our platform can generate a heat map for potential patients across the US, then deeper resolution at the state level, three-digit ZIP code level, and site level, including data on site experience, competing trials, and patient counts filtered by inclusion/exclusion criteria. This is precision operations, starting with the patient in mind. Thank you for being with me for some time. I will now turn the call over to Ben Taylor for the financial results.

Ben Taylor: Thanks, Najat. 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 toward a specific, quantifiable outcome, and that is 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 different partner milestones, and really investing behind the growth in our platform as well.

All of that comes back to focusing on those investments across our pipeline and technology portfolio that have the best risk/return and are going to give us the most impact for the investment that we are making. That is how we were able to have a 35% year-over-year reduction from pro forma 2024 to 2025 and even come in 10% below the guidance that we originally provided in May. We ended the year with $754 million in cash. Looking forward, operating expenses, our 2026 cash OPEX, are expected to be under $390 million. Cash operating expenses is a non-GAAP measure that we are 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 partnerships and transaction costs, you will be able to get directly to this guidance number that we are using. In addition, last year, it was really exciting to see that we crossed $500 million in cumulative partner inflows. We expect to continue to achieve those going forward.

In fact, we hit our first milestone earlier this month already, and we do include probability-weighting of some of those milestones in our cash flow projections going forward. Not only were we able to exceed our efficiency expectations, but that actually means we are extending our cash runway. We are updating our guidance to go to early 2028 as of now. With that, I will hand it back over to Najat.

Najat Khan: Thank you so much, Ben. We will wrap up by looking ahead. We have a very broad set of catalysts coming up, and it is going to be a busy next 18 to 24 months. In terms of this year, we are on track for our initial engagement with the FDA on REC-481. We are looking forward to that, and also initial data—early safety and PK—for RBM39, and go/no-go decisions for PI3K and ENPP1, which are both in IND-enabling. We will also have additional data for REC-4881 early next year, combo data expected for our CDK7 program, as well as more early safety and PK data from MALT1 and LSD1.

Recall for both of those, we designed the assets to be more tolerable, so these are going to be important. Partner catalysts will be very important. Our partnership with Sanofi has multiple programs progressing into more later-stage development candidate and other milestones. In addition, these maps, where novel biology is extracted into new programs with Roche and Genentech, are really important work 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, and, as Ben just mentioned, pairing all of that important work with disciplined execution.

We have really pivoted toward an outcomes-based budget where we test what value creation every dollar can drive—doing more with less. I will close by saying thank you so much for the time. Our focus will always remain on value creation for patients—they are the ones that we ultimately serve—and also, of course, our shareholders. Thank you again for listening. We will now open for questions. I will also have our CSO, Dave Hallett, joining us as well, in addition to Ben Taylor, to address some of the questions.

Sean (Morgan Stanley): There are many questions around REC-4881, understanding what the potential registrational pathway may look like upon alignment with the FDA, how you are thinking about providing a regulatory update, and updated patient population.

Najat Khan: It is a long question, so I will break it into pieces. In terms of the regulatory update, as I mentioned, we are on track for that initial engagement with the FDA in 2026. That is going to be really important in terms of the potential design for the registrational study, patient population, and endpoints. We have a very compelling dataset in terms of durability and polyp burden reduction. In addition, we also have the natural history data. Coupled together, it is going to be really important for us to have conversations with the FDA.

On the updated patient population, as I mentioned, 18 and over is already recruiting, as well as dose optimization schedules given what we saw with our durability data. More data on that is coming in 2027. As we have meaningful updates across both fronts, as you have seen, we have done webinars ad hoc. When we have more meaningful outcomes and updates, we will share with the Street as well.

Alex (Bank of America): It looks like the cost optimization measures really started to take hold in Q4. Any one-offs that helped in the quarter, or are these levels the expectation going forward?

Ben Taylor: Sure. Happy to. Yeah. Thanks, Alex. I agree with your data. It is really about efficiency more than cost cuts. We have hit a point where we have gone through all of the integration. I would assume that is all complete. There are no big one-offs on the system. What we really try to do is come in with an 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, you start to find ways to do more with less.

That is where we expect to be able to continue growing our pipeline, investing heavily behind our platform, and moving things forward while still hitting the cost targets that we put out there.

Najat Khan: Great. Thank you, Ben. The only thing I will add, Alex, is the piece around rapid go/no-go decisions and how we are doing that—the mentality and the mindset—and also understanding the variety of areas we are working on and the value proposition across the different areas, which evolves as you generate more data. Thinking like an investor is really important—being agile around capital allocation—and that is what we will continue to do, driven by data.

Unidentified Analyst: NVIDIA—what is 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 have trained?

Najat Khan: It is 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 seen we are going to be highlighted in a lightning round for NVIDIA’s upcoming GTC presentation, with Recursion Pharmaceuticals, Inc. being a pioneer in how to leverage automation. This wet and dry lab is not just words; this is how we do millions of experiments a week. Our collaboration with NVIDIA on Runner-2, one of the fastest supercomputers in life sciences, underpins our work—such as the PI3K examples using machine learning and molecular dynamics. Our partnership with NVIDIA could not be any stronger, so that continues.

In terms of the divestment, if you look at the public 13F filings from 2025, it is a shift in NVIDIA’s investment portfolio toward larger, on-strategy supercomputer and data center efforts. We were not the only company; other decisions were made as well. It is a collective shift toward large, billion-dollar-plus investments. On seeking other technology partners, we have a strategic partnership with Google for cloud compute. We have a partnership with NVIDIA on machine learning, models, and on-prem compute. We have always been one of the pioneers in bridging tech and science, and we will continue.

Georgia: With the recent positive preliminary efficacy data for REC-481 in FAP and the achievement of your fifth milestone with Sanofi, what specific metrics or historical comparisons from your current clinical portfolio best demonstrate that Recursion Pharmaceuticals, Inc. is improving the probability of clinical success or speed of development compared to traditional discovery methods?

Najat Khan: I am going to hand it over to Dave Hallett to get us started, and we can add more comments as well.

Dave Hallett: Thank you, Najat, and good morning and good afternoon to those of us in Europe. I will start from the discovery perspective. During the last presentation, Najat highlighted a number of themes: repeatability of delivery, specifically highlighted in the burgeoning Sanofi pipeline that we are building together. This is a repeatable platform delivering both best-in-class and first-in-class challenging targets. We also highlighted the speed of delivery. If you look at the metrics that we are delivering in terms of the numbers of novel compounds that we synthesize and test, and the speed that we are getting to development candidates, these are further demonstrations of the role technology plays in accelerating delivery.

The proof is ultimately in the clinic, and we are very excited for patients in terms of FAP. This is the first example from our platform where we have been able to demonstrate that a compound that came from Recursion Pharmaceuticals, Inc. has shown clinical proof of concept. The goal over the coming months and years is to show repeatability in that frame as well.

Najat Khan: Thank you, Dave. To add a broader perspective, Recursion Pharmaceuticals, Inc. has five-plus clinical programs, a diversified portfolio on the clinic side, and a diversified portfolio on the discovery side. It takes time and effort to build a platform. These datasets did not exist. The models did not exist. We are not a one- or two-asset biotech. We are a tech-bio for a reason, which is the repeatability and scalability—making all of this much more engineering-focused, using agentic agents or automation to do things better and faster, taking toil out of systems so we can supercharge our scientists to do the hard work of drug discovery and development. Drug discovery and development inherently is probabilistic—most things do not work.

We have a 90% failure rate. We know that multiple shots on goal are important. Two worlds—tech and bio—have not really come together before. We are not just building models that are interesting, but actually applying models that unlock value. We are constantly looking at metrics and stats—the green shoots—whether it is the number of compounds we synthesize (90% less than industry), the speed, the cost of our INDs. We do the same in the biology platform and in clinical development, where we are seeing improvement in enrollment and so forth. There is much work to be done, but this is what 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 importantly to patients that are willing to take a bet on us and our programs and that are waiting. We are working as hard as possible to forge a new era of how medicines are made for patients that are waiting. Thank you again for joining us today, and we look forward to sharing more.

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