Predict. Explain. Discover.

Valence Labs is Recursion’s AI research engine. Leveraging the full power of Recursion’s platform, data, and computing infrastructure, we develop new ways to predict, explain, and ultimately decode biology.

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Our focus

Predict

Building on more than a decade of experience in perturbative biology at Recursion—from scaled phenomics and transcriptomics datasets to multimodal foundation models—we are modeling the functional response of cells to perturbation at unprecedented scale.
Explain

Perturbations rewire cellular function by altering molecular interactions—binding, signaling, dynamics, and more. By combining interventional data with novel methods for predicting and simulating molecular interactions at scale, we are generating casual explanations for how molecular interventions shape cellular function.
Discover

Using lab-in-the-loop engines of biological discovery, we bridge functional readouts with mechanistic understanding to generate, test, and refine novel therapeutic hypotheses to accelerate and improve drug discovery outcomes.

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A unique set of industry-leading ingredients

Unprecedented Data Generation

Recursion’s OS houses automated biology and chemistry labs capable of generating enormous interventional datasets, today spanning > 60 petabytes of data across phenomics, transcriptomics, and other modalities.

Massive Computing Power

Using Recursion’s BioHive, the pharmaceutical industry’s leading supercomputer, we have the ability to run training and inference workflows at industry-leading scale.

World-Class Talent

With a mission-driven, interdisciplinary team, equally fluent in computer science and biology, we have the right people in place to realize our ambitious goal of decoding biology to radically improve lives.
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Our perspective

A vision for virtual cells—mechanistic models of cellular function that guide the discovery of novel therapeutics.
Read the Perspective

Virtual Cells: Predict, Explain, Discover

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Abstract

The objective of drug discovery is to accurately infer the effects of treatments on patients. Drug discovery would therefore be greatly improved if there existed computational models that
accurately predicted the response of patients to interventions, since this would allow
practitioners to safely and economically test and optimize a wide range of therapeutic
hypotheses before ever starting a human clinical trial. Even a more “modest” model that could
accurately predict the functional response of a wide variety of cells to genetic and chemical
interventions would be of tremendous value in designing effective and safe therapeutics more
likely to produce positive outcomes in the clinic. Creating such virtual cells has long been a goal
of the computational research community that even today remains an ambition due to the
daunting scale and complexity of the biomolecular interactions mediating cellular function.
Nevertheless, a confluence of technological advances suggest that there has never been a
better time to attempt to build virtual cells. In this perspective, we describe Valence Labs’s vision
for virtual cells as a transformative platform to indus drug discovery. We set the context
for our vision by reviewing historical progress, and outline their integration within a larger
framework of agentic systems that continuously refine our mechanistic understanding of human
physiology. We highlight recent advances in machine learning, computational power, and data
generation that now enable robust simulation of cellular functional responses, and we outline
key modeling considerations, evaluation benchmarks, and a roadmap for future research.