Recursion Pharmaceuticals Reports Q2 2026 Results: Full Earnings Call Transcript

Recursion Pharmaceuticals (NASDAQ: RXRX ) reported second-quarter financial results on Wednesday. The transcript from the company's second-quarter earnings call has been provided below. This content is powered APIs. For comprehensive financial data and transcripts, visit Access the full call at Summary Recursion Pharmaceuticals reported significant progress with its AI-native product engine, advancing five clinical-stage programs, including REC 4881 in FAP, and showcasing successful partnerships with Sanofi and Roche/Genentech, generating over $500 million in realized inflows.

The company emphasized the unique combination of capabilities that set its AI platform apart, including proprietary multimodal data, a lab-in-the-loop system, and the conversion of these into differentiated assets, resulting in improved speed and capital efficiency in drug discovery. Financially, Recursion Pharmaceuticals announced a reduction in 2026 cash operating expense guidance to $375 million, reflecting a nearly 40% decrease from 2024, while maintaining a strong cash position of $557 million, providing an operational runway through early 2028.

Full Transcript Najat Khan, Chief Executive Officer & President, Member, Board of Directors Good morning everyone, and thank you for joining us. Before we begin, I'd like to remind everyone that today's discussion will include forward-looking statements. Next slide. Please refer to today's press release and our SEC filings for additional details.

At Recursion Pharmaceuticals, our mission is to decode biology to radically improve patient lives, and we do this by building transformational medicines with an AI-native product engine. Over the past year, we have reached an important inflection point. We are no longer just discussing the potential of our platform; we are demonstrating the ability of our AI-native product engine to generate differentiated programs and medicines. Just as a reminder, the engine you see on the left-hand side is built as a continuous learning system.

Proprietary multimodal data created in our data factory powers frontier AI models, and these models then generate new hypotheses where every single prediction is tested experimentally. Each cycle strengthens both the engine and the products it creates. Ultimately though, the measure of any engine is its output. So let's talk about that.

First, our internal pipeline continues to mature. We now have five clinical-stage programs, including REC 4881 in FAP, where we have generated some of the most promising clinical data in the company's history. Remember, in a disease with no approved therapies and a TAM of almost 10 billion. Second, we continue to make significant progress in our partnerships while learning from the best in the industry and also while validating our engine externally.

Together with leading biopharma partners, we have generated more than 500 million in realized inflows while advancing differentiated programs with Sanofi and Roche/Genentech. So today I'll share how we continue to strengthen our product engine and how we are translating it into differentiated medicines, differentiated partnerships, and ultimately better outcomes for patients. So the question that naturally comes up: what makes our product engine different? There are many companies applying AI to drug discovery.

We believe our advantage isn't AI alone. It's the combination of three capabilities that reinforce one another. First, we generate our own proprietary multimodal biological and molecular data at scale. This matters because AI can only learn well from high-quality data, and much of the most valuable biology has never been measured systematically.

Our 50 petabytes of data is designed specifically to train models, discover new biological relationships, and improve over time as new algorithms emerge. Second, we connect these models directly to experimentation through a lab-in-the-loop system spanning biology, design, and increasingly the clinic. Every prediction, as I mentioned before, is validated experimentally. Every result feeds back into those models.

It is that recursive loop that helps us move faster, improve our decision quality, and systematically build confidence in our programs. And third, and most importantly, we convert these capabilities into differentiated assets. That includes both our internal clinical programs such as REC 4881 in FAP, REC 1245, RBM39 in solid tumors, as well as our partner programs with Sanofi, Roche, and Genentech. So how are we doing?

Let's look at the progress we've made year-to-date. As we look back over the first half or so of the year, I'm very pleased with the progress we're making across all three dimensions of our business: our internal pipeline, our partnerships, and the continued advancement of our AI-native product engine. On the internal pipeline, we advanced REC 4881 with our initial FDA engagement following encouraging Phase 2 data, and additional Phase 2 data coming later this year that Vicki will talk about shortly.

We have continued to build confidence in REC 1245 with early clinical safety and pharmacokinetic data, and we just received IND clearance for REC 7735, positioning it to enter the clinic later this year. At the same time, our partnerships are also making progress. As you'll remember from earlier this year, we achieved another milestone with Sanofi, our fifth to date, on developing a novel lead series for a very challenging first-in-class oncology target. But I'd like to pause on a new milestone in particular that we're announcing today together with Roche/Genentech.

We are thrilled to announce that Genentech advanced the collaboration's first neuroscience target—a new, unexplored target in neuroscience—into a joint early discovery program, providing early evidence that Recursion Pharmaceuticals' platform can generate novel, biologically validated targets for drug discovery. To me, this represents much more than another partnership milestone in an area where progress has been slow for decades.

It provides early evidence that a fundamentally different approach—combining proprietary disease-relevant atlases, purpose-built foundation models, and the rigorous computational and experimental assays that we use to build confidence that these targets are actually causal—and of course, last but definitely not least, the deep collaboration, scientific and technical, with a partner can uncover previously unexplored therapeutic targets. While it's still early, I believe this is an important proof point for both Recursion Pharmaceuticals and the broader field.

It suggests that an AI-native engine can move beyond optimizing known biology to discovering new biology compelling enough to advance into drug discovery with one of the world's leading neuroscience organizations. So that's just the left-hand side, but we have a lot more coming ahead.

For REC 4881, we will present additional Phase 2 data at the CGA IGC conference, a premier medical congress for inherited GI disorders, our specific target audience for FAP, and we will also provide an update on our FDA interactions as well as continue advancing what we believe could become a transformational therapy for patients with FAP—remember, nothing approved to date, no approved therapies. For REC 1245, we are continuing our dose escalation and generating additional Phase 1 data, and we'll have a more wholesome update later this year.

With Sanofi, we expect the potential nomination of an oral INI development candidate, a very important milestone that would further validate our ability to design differentiated small molecules against challenging targets with the potential to impact multiple immune-mediated diseases. And finally, we expect to initiate the Phase 1 study for REC 7735, further expanding our clinical oncology pipeline with another precision design program from our engine. Taken together, these milestones reflect a company that is delivering ambitious proof points that matter while executing with focus and discipline. But equally important, we continue to strengthen the engine itself.

Let me show you a few examples of how that innovation across biology, chemistry, and clinical development is making our engine faster and smarter. Let's start with biology. One of the biggest challenges in the industry is that much of human biology remains unscored. We believe the answer isn't simply building larger AI models; it's generating proprietary disease-relevant data that these models can actually learn from.

To do that, we have generated and aggregated more than 50 petabytes of multimodal biological data, creating what we believe is one of the largest proprietary datasets in the industry. As that dataset grows, our models become better at discovering novel biology, and every new discovery further strengthens the engine. That learning then carries into design. Because our biology models generate higher-confidence hypotheses, our chemistry platform focuses on designing better molecules more efficiently.

There's much to share here, but one thing I'll mention is we are advancing candidates using roughly 330 compounds over approximately a year and a half—so going from target to candidate in a year and a half—compared with industry benchmarks for small molecules of roughly 2,500 compounds over four years. That's a meaningful improvement in both speed and capital efficiency. And finally, we extend that same philosophy into the clinic. Clinical development is where a lot of value is ultimately created and where also a lot of programs fail.

By bringing AI into trial design—picking the right patients, I can't emphasize that enough—and site selection, we're already seeing improvements in enrollment speed and patient matching, helping us run smarter and more efficient studies. But one more important point: this isn't three different capabilities. It's one continuous learning system. Every experiment improves our data.

Better data improves our models. Better models make better molecules. And then clinical data is fed back into the system to make the next generation of products even stronger. Perhaps the best example of the flywheel in action is what we have demonstrated with Roche/Genentech and we're announcing today, where our biology engine discovered a previously unexplored and new neuroscience target.

I'd like to spend a few minutes just to take you behind the scenes as to how we got there and why we believe this represents an important new approach to discovering medicines together with Roche/Genentech. As we worked in this area to discover a new, unexplored target from our AI-driven map of biology, we focused on a few specific elements. Why does that matter? First, this wasn't about finding another target within well-studied biology.

It was about uncovering previously unexplored biology and building enough evidence experimentally to advance it into drug discovery with one of the leading neuroscience organizations in the world. Second, we believe this validates something bigger than a single target. It provides early evidence that when you combine the right data, build the right models, do very rigorous computational and experimental validation, and pair that with the right complementary collaboration, you can actually systematically uncover novel biology. And we believe this is just the beginning.

The underlying biological maps are reusable—this is a really important point—with the potential to generate many more therapeutic opportunities over time. Finally, across our collaboration with Roche/Genentech, we've now achieved more than 216 million in upfront and milestone payments, with the opportunity for more than 300 million in additional development, commercialization, and sales milestones for each future small-molecule program. Alright, so let me show you how we built this engine. To understand why this milestone matters, the question is: why neuroscience?

It's worth stepping back and asking that question. Neuroscience remains one of the greatest unmet needs in medicine. More than 3 billion people worldwide are affected by neurological diseases, and yet CNS drugs, as we know, continue to have amongst the lowest approval rates in industry. Neuroscience is particularly challenging because the biology is extraordinarily complex, difficult to model, and we have repeatedly returned to the same small set of well-understood targets with only incremental success.

We believe meaningful progress will require discovering new biology, not just simply optimizing what is already known. And that's exactly what this collaboration was designed to do. So the next question comes: what does it actually take to discover a target that people will have confidence in? And before I go into the details, just a huge, huge thank you to Roche/Genentech for this deep shoulder-to-shoulder collaboration.

It's one of the few rare ones that I've seen where the teams are looking at the same data, the same models, going through what validation needs to be done. So that joint collaboration was critical here. So everything starts with disease-relevant biology. We asked ourselves a simple question: are we studying neurons in a context that actually reflects human disease?

In our case, that meant creating iPSC-derived neuronal cells—both neuronal and microglial cells—at an unprecedented scale: more than a trillion neurons and hundreds of billions of microglia. What this does is it creates a rich, disease-relevant atlas that can be reused again and again to discover multiple future targets. We view this atlas as one of the most important long-term competitive advantages. But generating proprietary data, while important, isn't enough.

The next challenge is making sense of it. Before asking the models to find something new, we grounded every analysis in causal biology that we understand today. So really grounding it in genetics, we introduced hundreds of disease-causing perturbations and anchored our searches around well-established drivers of neurological disease. That matters because it gives every subsequent prediction of biology from a causal target from the very beginning.

Rather than searching blindly across the genome, we are searching from a foundation grounded in causal genetics and disease biology. Now, once that's established, AI can help us—and our foundation models—ask a much more interesting question: what is not seen? What can be unexplored biology that we don't know of today? This is where our foundation models come in.

Instead of evaluating one hypothesis at a time, the models compare the biological signatures of more than 17,000 genes across tens of millions of data points. They build relationships across the entire genome and identify genes that consistently behave like known disease drivers, even if they've never been implicated in that disease before. That allows data and foundation models—not preconceived hypotheses—to compile a prioritized list of new, novel potential targets. Now, AI can generate hypotheses, but medicines and programs require evidence.

Together with Roche/Genentech, we looked at every predicted target and then put that through a rigorous experimental validation cascade. We build confidence in layers. First, we establish that the target actually sits in the right biological pathway. Second, we show that changing the target can improve cellular function—for instance, neurons or microglia.

And finally, very critically, we demonstrate that this target and modulating it can meaningfully affect disease-relevant biology using multiple orthogonal assays. These assays are very robust, but they also include other multi-omic data layers such as proteomics, transcriptomics, etc. While no single experiment tells the story, what we do here is build a body of causal evidence before advancing the target.

So, putting it all together, our collaboration combines four capabilities: generating disease-relevant biology at unprecedented scale—and it's challenging to do, to actually have a trillion iPSC-derived neuronal cells that are high quality, standardized, viable; it takes a lot of specialized protocols and know-how to do that. Second, we use foundation models to systematically explore that biology. Third, we navigate from well-understood disease mechanisms towards previously unexplored new biology. And finally, a very important step is validating all of these predictions experimentally before we advance it.

So our first neuroscience target, as I mentioned before, has now advanced into a jointly developed small-molecule discovery program supported by our design platform. And again, what excites us most is of course this target, but the fact that this kind of data is highly reusable—the potential to mine it over and over again for unexplored targets—and also that this wasn't the result of one algorithm or one experiment. It's the result of a new operating model for discovering medicines. Before I hand it over to Vicki, I would like to highlight, as we move on to our internal programs, the pipeline.

As you can see here, we have multiple programs in the clinic. We're constantly looking at the data to make data-driven decisions for REC 4881 in FAP—where there's no approved therapies today—and REC 1245 targeting RBM39, a novel first-in-class target, first-in-class degrader. With limited clinical competition to date, combined with additional internal and partner assets, we believe this creates a diversified portfolio with multiple opportunities to create value in the coming years. With that, I'm going to turn it to Vicki to walk you through the internal pipeline in more detail.

Vicki Goodman, Chief Medical Officer Thank you, Najat. I'll start off this morning by talking about our REC 4881 program in FAP. FAP is a rare disease that requires lifelong management. Patients with FAP develop hundreds to thousands of adenomatous polyps in their GI tract and require colectomy to reduce the risk of colorectal cancer.