"This is only possible because we control the design, make, and test steps within one connected system rather than handing off between disconnected tools and teams." Once molecules are designed in silico, they must be synthesized and tested in the real world. In Part 7 of our platform series, VP of Pharmacology Martin Redhead, Ph.D., explains how these candidate designs move directly into our automated DMTL (Design, Make, Test, Learn) labs. Recursion’s automated chemistry platform goes far beyond simple one-pot reactions. We execute complex, multi-step medicinal chemistry at scale. Advanced automation handles the workup, purification, and digital sample tracking for thousands of compounds, creating a digital record of every single experiment. Our in-house AI agents take this a step further by actively helping scientists plan efficient synthetic routes. Because we control the entire design, make, and test lifecycle within one fully integrated system, we completely eliminate the friction and time lost handing off data between disconnected teams. 🚀 Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #DrugDesign #ComputationalChemistry #DrugDiscovery #MachineLearning

Recursion Announces New Clinical Data Across Two Potential First-in-Class Programs, Sanofi Partnership Milestone, and Oversubscribed $200M Offering Today, we announced important progress across our clinical pipeline, partnerships, and capital position—reflecting growing evidence of what our integrated AI-enabled drug discovery platform can deliver. 🚀 REC-4881 | Deep, durable polyp reductions in FAP In an expanded adult population (18+), 87% of patients achieved polyp burden reductions after 12 weeks (47% median), with 85% remaining below baseline after 12 weeks off treatment. In the upper GI—the area of greatest unmet need—92% achieved reductions (55% median), including two patients with near-complete reductions (≥98%). Of those with reductions, 92% remained below baseline after 12 weeks off treatment. With no approved pharmacologic therapies for FAP, we are targeting registrational trial initiation in 2H 2027, pending FDA discussions. Full data will be presented November 2 at CGA-IGC. 🚀 REC-1245 | Encouraging early activity in heavily pretreated cancer patients Our potential first-in-class oral RBM39 degrader demonstrated three partial responses (two confirmed in endometrial cancer; one unconfirmed in liver cancer) and 91.7% disease control (11/12) at doses ≥45 mg. No dose-limiting toxicities or treatment-related serious adverse events were observed at the selected 45 mg expansion dose. 🚀 Sanofi | First development candidate optioned Two challenging targets, one oral molecule. A potential first-in-class candidate for inflammatory disease, enabled by Recursion’s AI platform and small-molecule chemistry capabilities alongside Sanofi’s expertise. Phase 1 expected in 2027, marking the sixth progress-based milestone across our collaboration. 🚀 $200M Oversubscribed Follow-On Offering | Extending Runway Into 2029 Broad demand from leading healthcare specialists, long-only institutions, and growth investors, including Janus Henderson Investors, Invus, Longaeva Partners, ARK Invest, and Driehaus Capital Management. The financing supports advancement of our clinical pipeline, continued AI engine innovation, and strategic partnerships. Multiple independent proof points. Across diseases, programs, and partnerships. Powered by one integrated AI-enabled engine. Thank you to our employees, collaborators, clinical investigators, patients, and investors who make this progress possible. Links below 👇 Business Update: ir.recursion.com/news-releas… Sanofi announcement: ir.recursion.com/news-releas… Public offering: ir.recursion.com/news-releas…

"Our integrated system narrows millions of molecules down to a short list that's synthetically accessible. Not just promising in silico, but actually makeable before a single one is sent to the labs." Traditional drug design often leans on decades of precedent and familiar chemistry. In Part 6 of our platform series, SVP of Drug Design Donovan Chin, Ph.D., explains how Recursion breaks that mold by getting a full computational design cycle up and running in about a day. Our AI-native engine vastly increases the aperture for our chemists. Instead of relying solely on known binding pockets, they can efficiently explore entirely novel and complex chemical space. By leveraging our integrated data, the team can forecast non-obvious design hypotheses and predict how to synthesize molecules even when nothing like them currently exists. This process begins by scoring binding affinity using advanced 3D structural models like Nesso-1. From there, the team runs hundreds of integrated models covering ADME, potency, advanced simulations, and AI retrosynthesis. This unified system rapidly filters millions of potential molecules down to a precise shortlist of high-quality candidates that are physically ready for the lab. 🚀 Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #DrugDesign #ComputationalChemistry #DrugDiscovery #MachineLearning

"Across our programs, we've advanced quality novel candidates after synthesizing only around 300 compounds, roughly 90% fewer than the industry average." Now that we have mapped the biology, we need to design the exact molecule to target it. In Part 5 of our platform series, SVP of Drug Design Donovan Chin, Ph.D., and VP of Pharmacology Martin Redhead, Ph.D., break down how we transition from biological target discovery to navigating complex chemical space. Just like our approach to biology, we solve chemistry problems through a continuous feedback loop between computation and experimentation. Before a single molecule is ever synthesized in the lab, our team uses Centaur, our integrated drug design environment. By combining hundreds of machine learning and physics-based models into a single platform, we can iteratively design novel molecular structures starting directly from target properties. This generative de novo design approach automatically updates our models so they improve with every single cycle. Our AI-native product engine has already produced over 10 development candidates. By holding our chemistry to the same rigorous standards as our biology, we can synthesize a fraction of the traditional compound volume while delivering superior novel molecules. We believe this focus on quality and novelty over sheer volume will define the true impact of AI on drug development. 🚀 Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #DrugDesign #AIDrugDiscovery #DrugDiscovery #Chemistry

Najat Khan, CEO of Recursion, will speak at Quorum, a healthcare AI leaders forum co-hosted by Flare Capital Partners and Google for Startups on Oct 14. Najat will be featured on the panel “From Molecule to Market: AI and the Future of Pharma R&D.” She will join Melisa Tucker, Chief Data Officer at Tempus AI, and Zach Weinberg, Co-Founder & CEO at Curie.Bio., to discuss where AI is actually compressing drug discovery timelines, reshaping clinical trials, and rewriting R&D productivity. Learn more here: 👉gatsby.events/flarecapital-p… #TechBio #AI #AIdrugdiscovery

"A hypothesis from a model isn't a discovery yet. So, it goes back into the lab. The biology the model predicted is the biology we saw at the bench." A computational prediction alone does not cure a disease. In Part 4 of our platform series, Associate Principal Scientist Celia Fernandez explains how Recursion closes the loop to turn AI generated hypotheses into validated biological targets. Once our foundation models point to a potential target, we run the lab in the opposite direction. The hypothesis enters a validation cascade that becomes progressively more demanding and highly specific to the disease. This physical testing allows us to aggressively derisk our computational discoveries directly at the bench. We test these targets against the exact same high bar that our global pharma partners hold their own internal programs to. When the loop finally closes, we can confidently prove that the biology the machine learning model predicted matches the physical reality we see in the lab. 🚀Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #AIdrugdiscovery #AI #drugdiscovery

“The data is used to train our models. The models point to the targets. That’s lab-in-the-loop.” Generating millions of data points is only the first step. You need immense computational power to make sense of it (and maybe a good pair of headphones, because BioHive-2 is loud). In Part 3 of our series on our end-to-end platform, Chief Technology Officer Ben Mabey explains how Recursion turns raw data into a novel view of biology. 🖥️Using BioHive-2, one of the fastest supercomputers in the world, we train state-of-the-art foundation models to process every image and sequence we generate. These models create a unique embedding, or fingerprint, for every cellular perturbation we have ever performed. All of this high-dimensional data is compiled into a single massive dataset to form a comprehensive map of biology. By comparing known disease fingerprints against this map, we can find other genetic manipulations that create similar effects. This allows us to identify targets that drive or rescue a disease state, generating new hypotheses and pointing us towards potential novel discoveries. 🚀Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #AI #drugdiscovery #AIdrugdiscovery #biology

On last week’s episode of TechBio Talks, CEO of Recursion, Najat Khan and @morgancheatham, Partner and Head of Healthcare and Life Sciences at Breyer Capital connected for a conversation spanning biotech value creation, building data moats, and the future of medicine. They unpacked: 🎯 Collapsing target ID and early patient selection to treat the root biological cause 🧬 Unlocking decades of tissue and diagnostic data sitting in clinical biobanks 🩺 Extracting hidden knowledge and research-grade insights from the routine clinical data we already collect every day Catch up on their full conversation here: YouTube: youtu.be/LmVszrLN0cg?si=9_lX… Spotify: open.spotify.com/episode/6BE… Apple: podcasts.apple.com/us/podcas… #AI #TechBio #drugdiscovery #AIdrugdiscovery #drugdesign #Biology

“This is the part that turns this from just a cell factory into a data factory. This high-dimensional data is the raw material our foundation models learn from. And because it’s this rich, the data is reusable, mined again and again across programs rather than generated once and thrown away.” Growing disease-relevant cells at scale is only the beginning. In Part 2 of our series on our end-to-end platform, VP of Neuroscience Chris Winrow breaks down how we run genome-scale perturbations and quantify the resulting changes in cellular state. 🔬Using acoustic liquid handlers, we move 2.5-nanoliter droplets of CRISPR-Cas9 reagents into 1,536-well plates, up to 2 million times a week. Crucially, we intentionally randomize these experiments so our machine learning models learn true biology rather than experimental artifacts. By imaging and sequencing these exact cells, we capture thousands of measurements from a single experiment. This creates a rich, reusable dataset that feeds our AI foundation models today and stays future-proofed for tomorrow. 🚀 Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #AI #drugdiscovery #drugdesign #biology #AIdrugdiscovery

🧠 “To build this genome-wide map, we had to create over 1 trillion neurons, roughly the equivalent of 12 human brains. There was no blueprint for this.” To truly study a disease, you have to work with cells that actually resemble it. In Part 1 of our new series breaking down our end-to-end platform, CEO Najat Khan shares the unprecedented engineering required to map biology at an industrial scale. Starting in 2021, we set out to grow human neurons at scale – executing systematic genome-wide perturbations, and capturing high-dimensional imaging. Using specialized proprietary protocols developed in partnership with Roche and Genentech, we kept these iPSC-derived neurons alive, consistent, and biologically representative. We are already seeing the impact of this work, with Genentech having optioned the first neuroscience target from the collaboration into a joint early discovery program. This is the foundation of high-dimensional target discovery. 🚀 Make sure to follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery.