This is like giving a superpowered microscope and a pattern-spotting robot to a drug lab. The system runs huge numbers of biological experiments, turns the images and data into a “map” of how cells react, and then uses AI to quickly suggest which molecules could become medicines, instead of scientists guessing and testing one-by-one over many years.
Traditional drug discovery is slow, expensive, and has a high failure rate because scientists manually design and test small numbers of hypotheses. Recursion’s AI platform aims to industrialize and automate the early drug discovery process—testing far more biological conditions, spotting patterns humans would miss, and prioritizing the most promising drug candidates earlier—thereby reducing R&D cost, time-to-clinic, and late-stage failures.
A large, proprietary, and continuously growing biological dataset (high-content imaging and omics) plus specialized AI models and lab automation tightly integrated into a single platform. This proprietary data flywheel—more experiments → better models → better programs → more funding for experiments—creates a barrier competitors cannot easily replicate without comparable data scale and infrastructure.
Hybrid
Vector Search
High (Custom Models/Infra)
Scaling wet-lab experimentation throughput and managing the cost/latency of generating and labeling massive high-content biological datasets; compute cost for large-scale model training is secondary to lab throughput and capital intensity.
Early Adopters
Compared to other AI-drug-discovery players that primarily offer modeling on public or partner data, Recursion tightly couples automated in-house wet labs with high-content imaging and proprietary data generation at scale. Its platform is positioned as a vertically integrated "techbio" stack: from experiment automation to data acquisition, representation learning, and internal pipelines of drug programs, rather than a pure software or services model.