Recursion

@RecursionPharma • 17,422 subscribers

Decoding biology to radically improve lives. The industrial revolution of drug discovery is here.

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Today, Recursion reports its second quarter financial results and business updates, showing demonstrated progress across partnerships, clinical programs, and financial goals. 🚀 Highlights include: ▪️Genentech advanced the collaboration's first neuroscience target into a joint early discovery program, providing early evidence that Recursion's platform can generate novel, biologically validated targets for drug discovery ▪️ REC-4881 (MEK1/2 inhibitor): Additional Phase 2 data in Familial Adenomatous Polyposis (FAP) to be presented at the Presidential Plenary session at leading hereditary GI annual meeting in November 2026 ▪️ REC-7735 (PI3Kα H1047R inhibitor): IND cleared, Phase 1/2 trial start in 2H26; a >100× mutant-selective PI3Kα H1047R inhibitor designed to improve therapeutic index by enabling deep suppression of the most common activating PI3Kα mutation while sparing wild-type PI3Kα ▪️Reduced 2026 cash operating expense guidance to <$375 million (from <$390 million) "Recursion has reached a pivotal point where our AI-native engine is translating unique data into potential first-in-class therapeutic opportunities," said Recursion CEO Najat Khan, PhD. "The advancement of the first unexplored neuroscience target from our collaboration with Roche and Genentech into an early discovery program is an important proof point. Finding new targets in neuroscience has historically been challenging, and this milestone highlights our ability to uncover novel biology in areas where conventional approaches have struggled." 💠 Recursion will host an Earnings Call today, August 5, 2026, at 8:00 am ET / 6:00 am MT / 1:00 pm BST. Join on: ▪️X: ▪️YouTube: ▪️LinkedIn: Analysts, investors, and the public have the opportunity to ask questions of the Company by submitting questions here:

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$🚀 A better, faster co-folding-based binding affinity model. Predicting how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:$