Valence Discovery

Valence Discovery

Drug Discovery & Development

About Valence Discovery

Valence Labs is Recursion's AI research engine dedicated to decoding biology through advanced computational methods. The company focuses on three core pillars: Predict, Explain, and Discover. They build on over a decade of experience in perturbative biology, developing models that predict functional responses of cells to perturbations at unprecedented scale using multimodal foundation models trained on phenomics and transcriptomics datasets. Their work combines interventional data with novel methods for predicting and simulating molecular interactions to generate causal explanations for how molecular interventions shape cellular function. Using lab-in-the-loop engines of biological discovery, Valence Labs bridges functional readouts with mechanistic understanding to generate, test, and refine novel therapeutic hypotheses. The company's ambitious vision centers on creating 'virtual cells'—mechanistic models of cellular function that can accurately predict patient responses to interventions before clinical trials begin. Powered by Recursion's OS automated biology and chemistry labs generating over 60 petabytes of data, BioHive supercomputer for massive-scale computing, and a world-class interdisciplinary team, Valence Labs aims to transform drug discovery by enabling safer, more economical testing and optimization of therapeutic hypotheses through computational simulation rather than traditional experimental approaches.

Mission & Story

Mission

Decoding biology to radically improve lives

Who It Serves

PharmaBiotechResearch

Milestones & Awards

Recent Milestones

Published TxPert model for predicting cellular responses to genetic perturbations (May 2025), launched OpenQDC open-source quantum datasets hub (November 2024), published Virtual Cells perspective paper (2025)

Awards & Recognition

BioHive described as pharmaceutical industry's leading supercomputer

Key Partnerships

Recursion (parent company)

Company Details

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Official Sources

Last updated Jul 1, 2026

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