“In its quest to transform how medicines are made, engineering, AI-pattern recognition, and cloud computing have proven as necessary as biochemical expertise.” A recent Case Study from @googlecloud highlights how Recursion is accelerating new treatments using the Recursion OS platform which “supports bursts of computational power that weigh in at trillions of calculations per second.” Combining a data-driven understanding of biology and chemistry with the latest in machine learning tools, the study notes that Recursion is advancing a pipeline of clinical stage drugs, including REC-4881, a potential first-in-disease treatment for the rare disease Familial Adenomatous Polyposis (FAP) that is one of the most advanced AI drugs in development today. “Recursion has reached an inflection point — from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value,” notes CEO and President Najat Khan. In addition to five drugs advancing through clinical trials in Recursion’s internal pipeline and with partners like @sanofi and @Roche and @genentech, the study notes that Recursion has developed a suite of state-of-the-art machine learning foundation models, and maps of biology both internally and with partners (including the world's first whole-genome Neuromap and Microglia Map) that are uncovering unknown biology in difficult areas like neurological diseases. Leveraging a hybrid computing approach – training models on Recursion’s supercomputer, BioHive-2, and running inference on Cloud GPUs and TPUs – has allowed Recursion to continue to scale efficiently. "The potential of using Cloud TPU pods to accelerate our research while keeping operational costs and complexity low is a big draw," says CTO @bmabey. 👉 Read more:  cloud.google.com/customers/r…

Donovan Chin is joining Recursion as Senior Vice President of Drug Design, effective June 22. His career spans small molecules, RNA-targeted therapeutics, novel peptide modalities, and AI-enabled discovery platforms. At Parabilis, Donovan led the computational drug discovery strategy behind Helicons, a novel class of constrained alpha-helical peptides that helped advance a first-in-class β-catenin inhibitor program to FDA Fast Track designation. At Arrakis Therapeutics, he pioneered computational approaches for RNA-targeted drug discovery, helping unlock small-molecule engagement of previously inaccessible RNA structures. Earlier at Novartis, he helped pioneer the application of computational approaches to drug discovery, building capabilities that supported a diverse portfolio across oncology, cardiovascular and metabolic disease, infectious disease, and chemical biology. Throughout his career, he has built high-performing multidisciplinary teams and advanced programs at the intersection of computation, chemistry, and biology – the same intersection where we believe the next generation of medicines will be discovered. Welcome to Recursion, Donovan!

One of the most important applications of AI is in improving clinical trials which encompass up to 70% of R&D costs and where most drugs in development fail. On June 17, 4:20pm at @HLTHEVENT Europe in Amsterdam, Recursion CFO Ben Taylor will present on the panel “Trials & tribulations: Exploring the next frontier in evidence generation,” looking at the rise of new clinical models that are shifting the traditional framework – including in silico models, digital twins, synthetic controls, adaptive frameworks, and hybrid or decentralized trials. He’ll be joined by Mati Gill, CEO of @AionLabs; Peter Donnelly, Co-Founder & CEO of Genomics; and Yajing Zhu, Director, Computational RWE at @novonordisk. Moderated by Adama Ibrahim, President, Digital Transformation at Crest Meridian, the panel will explore how clinical trials have evolved, expected breakthroughs on the horizon, and how pharma can prepare for a world where evidence generation is faster, more diverse, and increasingly augmented by technology. 👉 Learn more: hlth.com/events/europe/agend…

💡“Just like we simulate molecules and we simulate biology, what if we could simulate our trials before we run them?” In this clip from CEO and President Najat Khan's conversation with Selina Koch for the @BioCentury Show, she talks about where AI is making an impact on clinical trials. 💠Recursion’s approach is focused in three areas, Najat says. 1. Improving patient stratification - “Can you improve the signal to noise to know which patients would better respond?” 2. Smarter protocol design and simulating the protocol. 3. Improving recruitment and enrollment. “80% of trials don’t recruit on time.” 👉 Watch the full conversation here: youtube.com/watch?v=LfGy7FvH…

Inside the AI drug discovery evolution. A new story from Thomas Macaulay in @RunInfiniteLoop highlights Recursion’s industrial-scale approach to mapping biology and how the AI drug discovery industry is evolving. As Recursion CTO @bmabey said: "AI gives us better insights earlier in the process so we have greater certainty that a program will succeed or fail in patients and to identify which patients will be most likely to benefit." 👉 Read more: infiniteloop.media/p/ai-drug…

🚀 AI drug discovery is in acceleration mode. A new article from @RealEndptsMel in @NatureBiotech highlights how next-generation AI companies like Recursion are transforming biology into an engineering discipline. Commenting on efficiency, Recursion CEO and President Najat Khan said: “On average, we go from project start to advanced candidate in 17 months.” That acceleration signals a fundamental shift. Across the industry, drug discovery is becoming data-driven, iterative, and scalable. The story highlights Recursion’s approach: ▪️ Leveraging machine learning to uncover previously “invisible” biological signals ▪️ Repurposing and advancing compounds with new mechanistic insight ▪️ Building an end-to-end platform that connects discovery, biology, and development It’s a critical point for the industry: Real impact comes from integrating AI across the full R&D lifecycle — from data generation to clinical execution. 👉 Read more: nature.com/articles/s41587-0…

🚀Clinical momentum, capital efficiency, and high-quality data. Ben Taylor, Recursion CFO and President of Recursion UK, sat down recently with Alec Stranahan at the @BankofAmerica Global Healthcare Conference, highlighting how Recursion’s data- and AI-led approach is building a more predictive, risk-diversified model to get better medicines to patients faster. 🔹 Here are three key takeaways: 1. It’s all about patient impact. As Ben noted, every part of the Recursion OS is designed to increase the probability of clinical success. "At the heart of it, we're a therapeutics company. And so we really focus on making sure that we're advancing the pipeline.” 2. Data quality is essential. As the industry rushes to train foundation models, Ben stressed that simply having massive amounts of data isn't enough. It’s about quality. "If you're just using public data or even if you're just using data that has been generated outside of the ML context, the fidelity of it is pretty low,” he said. “A much smaller data set of highly annotated data actually creates far more predictive models than a massive data set of poorly annotated data." 3. Driving capital efficiency with technology. Recursion is leveraging technology not only to advance new medicines, but to radically improve the unit economics of drug discovery – applying a strict analytical framework to ensure that capital flows directly to value-driving programs. “We should always be getting more impact for less cost,” Ben said. “Right now, most people don't realize this, about two-thirds of our cost is going directly into pipeline programs and our partnerships." With continued pipeline momentum and catalysts across multiple clinical programs over the next 12-18 months, we are demonstrating clear proof points for our data- and AI-led approach. 👉 Catch the full session here: bofa.veracast.com/webcasts/b…

Join us at the AI Compute Summit in Amsterdam! Maureen Makes, VP of Engineering at Recursion, is speaking May 19 on the panel “People, power and performance" at the 2nd annual AI Compute Summit in Amsterdam from @TheEconomist Enterprise. Along with Marina Antoniou, Global Markets Head of Market Abuse Risk Assessment at @NatWestGroup; Rui Oliveira, Director of the Minho Advanced Computing Centre; and Valeriu Codreanu, Manger of Compute Services at SURF, they’ll discuss how AI infrastructure is expanding faster than the talent to run it, and how organizations can reskill teams and develop leadership for a world defined by GPUs, automation and data governance. The panel will be moderated by Matus Samel, Principal of Energy & Sustainability at Economist Enterprise. 👉 Learn more and register: events.economistenterprise.c…

🧬 Bridging the gap in AI drug discovery. @AlisandraDenton, Staff Machine Learning Scientist at Recursion and one of the authors on our recent paper in @NatureBiotech, explains how the AI model TxPert predicts how a cell will respond to perturbations. Predicting a cell’s RNA activity, or transcriptome, is key to bridging the gap between cellular changes and clinical outcomes and advancing the potential for AI drug discovery. As Ali says, “with hundreds of cell types and so much disease variation, the total possibilities are too vast to measure in a lab.” She describes how TxPert allows us to perform a “Virtual Assay,” taking the mathematical signature of a healthy cell called the Basal State and adding the perturbation’s embedding to deliver a highly accurate prediction of what the cell’s transcriptome will look like after treatment. TxPert uses layered graph-based models that integrate phenomics — or how a cell looks — and transcriptomics — which genes are expressed — along with massive public biological knowledge resources. The model can even predict how a perturbation will work in entirely new cell lines it hasn’t seen before as well as accurately forecast the effects of “double perturbations,” consistently identifying "unknown unknowns" that traditional models — and even massive general-purpose AI — often miss. Ali notes that TxPert is currently predicting genetic perturbations, but more flexible models —  including those predicting drug effects — are in the works. 👉 Check out the full paper in Nature Biotech: nature.com/articles/s41587-0…

Recursion Chief Scientific Officer Dave Hallett will give a keynote presentation at Drug Discovery Europe on June 15, 9am in Berlin. In a talk titled “The Automation & AI Landscape In 2026,” Dave will demystify the term “lab-in-a-loop” and detail some of the major advances that have been made in autonomous science and drug discovery. He’ll share how Recursion is operating at the interface between the physical world and the virtual world and how the company has built the infrastructure required to enable agentic orchestration of the AI-driven, iterative, design-make-test-learn approach that refines small molecule therapeutics into highly optimized drugs for challenging targets in diseases with high unmet need. 👉 Learn more and register here: oxfordglobal.com/discovery-d… @OGConferences

“What’s the one question we obsess over at Recursion? ‘How do we harness the full power of AI to consistently and with urgency create better medicines for patients’?” In a recap of Recursion’s recent 1Q earnings, CEO and President Najat Khan talks about the tangible evidence that Recursion’s AI platform is delivering. 🔹 They include: ▪️  REC-1245, a potential first-in-class oncology program where both the biology and molecule were discovered using the Recursion OS. Early clinical data shows a well-tolerated profile with no dose-limiting toxicities and an encouraging PK profile, with more data expected in the second half of this year. ▪️  REC-4881, a potential first-in-disease program for the rare disease FAP that has already shown strong proof of concept with meaningful and durable impact. We’ve now initiated engagement with the FDA on a potential path to registration, with an update expected in the second half of this year. ▪️  REC-4539, a potentially best-in-class LSD1 inhibitor for small cell lung cancer and AML in which the first patient was recently dosed in the Phase 1 trial. “This is a precision-designed molecule,” Najat says, “built to address class-limiting toxicity” with the potential for “improved safety and CNS penetration.” It’s not about one asset, Najat says, but building a “repeatable, AI-driven product engine that’s starting to deliver across discovery and into the clinic.” 👉 Read Recursion’s full earnings report here: ir.recursion.com/news-releas…

"Bigger isn't always better.” — Dave Hallett, CSO of Recursion At the recent @SynBioBeta event, Dave joined fellow industry leaders from @Xaira_Thera, @GSK, @NOETIK_ai and @nvidia to discuss one of the most pressing issues in AI drug discovery: "Solving the Scale Mismatch Between Cells and Patients in Virtual Biology." How do we better translate cellular insights to patient outcomes using AI – and change drug discovery’s 90% failure rate? 💡 Key insights include: ▪️ Context Is Everything: “It is important to generate high quality, high value, large perturbational datasets, but you also need to do that in context,” Dave said. That’s why Recursion is increasingly moving toward multimodal data generated in highly specific contexts. ▪️ Engineering Needs to Match Human Reality: As Dave pointed out, the closer a model gets to a human, the harder it is to scale perturbational data. Recursion is bridging this gap by moving toward complex, engineered systems like iPSC-derived neurons and engineered cancer cell lines, and then, “Perturbing those at genome scale. And then integrating in over a dozen cellular systems,” merging phenotypic, transcriptomic, and proteomic data. ▪️ Quality Over Scale: Dave noted that while massive public datasets like single-cell transcriptomics exist, the variation in how that data was collected across different labs often results in models that memorize technical noise rather than biological truth. To build accurate foundation models—like Recursion’s recently announced state-of-the-art transcriptomic foundation model known as TxFM—he said we need high-quality, standardized, multimodal data and model architecture that accurately reflect unordered, interconnected biological states. Thank you to @StacieCT for moderating an excellent discussion, and to Marc Tessier-Lavigne, Kim Branson, and @Ronalfa for sharing their perspectives on the future of virtual biology.

After a decade of hype, Najat Khan is bringing Recursion back down to earth fiercebiotech.com/biotech/af…

Today, we announce our Q1 2026 business updates and financial results – demonstrating continued momentum across our internal portfolio and partnered programs, with multiple value-driving milestones on track. 🚀 Key proof points include: ▪️ REC-1245 (RBM39 degrader): Early clinical data demonstrate a well-tolerated safety profile and predictable, dose-dependent PK (n=16); dose escalation ongoing with no dose-limiting toxicities observed to date. ▪️ REC-4539 (LSD1 inhibitor): First patient dosed in Phase 1; platform-derived, selective, brain-penetrant profile with a reversible mechanism and shorter predicted half-life aimed at reducing on-target platelet toxicity, supporting differentiation in solid tumors and AML. ▪️ REC-4881 (MEK1/2 inhibitor): Strong Phase 2 efficacy signals and a safety profile consistent with the MEK inhibitor class, with FDA engagement initiated to define a potential registrational pathway and an update expected in 2H26. ▪️ Our joint programs with @sanofi continue advancing towards development candidate designation and earlier stage program milestones in the next 12 months, and we expect to continue to translate biological insights from maps delivered to @Roche and @genentech into potential target validation milestones over the next 12 months. As CEO and President Najat Khan says, this “represents a growing set of proof points that demonstrate our ability to translate platform insights into clinical programs. This progress reflects the strength of both our internal pipeline and partnerships, with multiple differentiated programs advancing through our end-to-end AI platform.” Our Q1 Earnings report also highlights our disciplined capital execution: reiterating the 2026 guidance of <$390M operational cash burn, and supporting runway into early 2028 without additional financing. 👉 Join our Earnings Call on Wed., May 6 at 8:00 am ET / 6:00 am MT / 1:00 pm BST, here on X and on: ▪️ YouTube: youtube.com/live/mnLb-AaPgHo ▪️ LinkedIn: linkedin.com/events/74552259… 👉 Analysts, investors, and the public can submit questions here: forms.gle/TQ4vgUTLKsFmikcu6. 👉 Read the full report: ir.recursion.com/news-releas…

A new story in @biospace from Annalee Armstrong captures the inspiring presentation from FAP advocate Jenny Jones, who recently visited Recursion with her father, Timothy. They “offered a powerful reminder of why they were all there,” the story notes. Familial adenomatous polyposis is a rare disease that requires a lifetime of surgeries and has affected the Jones family for generations. It often begins in childhood, and results in hundreds to thousands of polyps developing in the colon and rectum that will turn cancerous if not removed. Jenny’s own FAP journey began with chronic abdominal pain at age 7. “She remembers a doctor telling her she was ‘whiny’ — despite the verified family history of FAP,” the story notes (both her mother and grandfather died from FAP complications). Jenny was diagnosed at age 8 and had her colon removed at 9. She would have eight surgeries throughout her life, including her gallbladder removed at age 36, when doctors found a polyp. The story highlights Jenny’s resilience in the face of not only physical but mental battles brought on by her disease and its complications. She has devoted her life to improving education and outcomes for other people living with FAP through her nonprofit, Life’s a Polyp Foundation, and she called on the assembled Recursionauts to continue to work toward non-surgical treatment options. 👉 Read the story in Biospace: biospace.com/drug-developmen… 👉 For full information about our program, see the press release here: ir.recursion.com/news-releas…

🧬 Closing the translation gap between cells and patients. 😷 @NatureBiotech just published a new paper from Recursion on TxPert – a deep learning framework that accurately simulates the transcriptomic shift in unseen biological contexts. TxPert represents an important step in our ongoing work to accurately model transcriptomics and bridge the gap between in vitro discovery and clinical reality – which is critical for improving and scaling AI drug discovery. 🔹 TxPert address this translational gap through: ▪️ Graph Neural Networks (GNNs): Rather than treating genes as isolated lines of code, TxPert uses an advanced Exphormer-MG architecture to map genetic perturbations across multiple, massive knowledge graphs, forcing the model to understand both the physical (phenomic) and molecular (transcriptomic) realities of a cell simultaneously. ▪️ Simulating the "latent shift": By mathematically applying a "perturbation embedding" to a cell's baseline state, TxPert can accurately predict the entire post-perturbation transcriptomic profile—without anyone ever having to touch a pipette. ▪️ Predicting unseen biology: TxPert successfully predicts the transcriptomic outcomes of completely unseen single perturbations, complex combinatorial therapies (Double Perturbations), and even how known drugs will act in entirely new, unseen cell lines. TxPert is one of several models at Recursion to model transcriptomics and close the translation gap between cell responses and patients in the clinic. 🎉 Congrats to the team! Frederik Wenkel, Wilson Tu, Cassandra Masschelein, Hamed Shirzad, Liam Hodgson, Ihab Bendidi, Cian Eastwood, Shawn Whitfield, Craig T. Russell, Yassir El Mesbahi, Marta Fay, @bertonearnshaw, @ENoutahi, and @AlisandraDenton. 👉 Read the full publication in Nature Biotech here: nature.com/articles/s41587-0…

We had a fantastic time with @valence_ai at @iclr_conf in Rio sharing our latest machine learning breakthroughs, including presentations on TxFM, our state-of-the-art transcriptomics model that outperforms models up to 100x larger in terms of data size, and MarS-FM, our new class of generative models for molecular dynamics simulations. And there were lots of great community conversations happening at the rooftop TechBio Social, co-hosted with ICLR’s Learning Meaningful Representations of Life (LMRL) Workshop. Coming soon: we’re looking forward to sharing more of our ML breakthroughs at @icmlconf! 👉 TxFM paper here: openreview.net/pdf?id=NqZqCl… 👉 MarS-FM paper here: arxiv.org/html/2509.24779v3