AI Drug Discovery
Recursion Pharmaceuticals
by Recursion Pharmaceuticals, Inc.
Decoding biology to industrialize drug discovery with AI and automation
Category
AI Drug Discovery
Founded
2013
Headquarters
Salt Lake City, UT, USA
Overview
Recursion Pharmaceuticals operates one of the world's largest proprietary biological and chemical datasets, combining high-throughput wet-lab experiments with advanced machine learning to map biological relationships at scale. The Recursion OS platform integrates automated cell biology experiments, large-scale phenomics imaging, and foundation models to identify drug candidates faster than traditional approaches. Pharmaceutical companies and biotech firms use Recursion's platform to discover novel therapeutic targets, repurpose existing compounds, and advance preclinical candidates across oncology, rare diseases, and infectious disease. The platform has generated over 36 petabytes of proprietary biological data, enabling AI models trained on billions of experimental data points. Recursion's key differentiator is its closed-loop system where wet-lab experiments continuously feed AI models, which in turn design the next round of experiments. The company has multiple clinical-stage programs and strategic partnerships with Roche-Genentech, Bayer, and others valued at over $1 billion in total deal value.
Key Features
Clinical Trial Prediction
AI models predict clinical trial success probability based on preclinical data and historical trial outcomes.
Multi-Target Optimization
Simultaneously optimize drug candidates across multiple biological targets for polypharmacology approaches.
ADMET Profiling
Comprehensive in silico prediction of absorption, distribution, metabolism, excretion, and toxicity profiles.
De Novo Drug Design
Design entirely new drug molecules from scratch using generative AI trained on billions of molecular interactions.
Binding Affinity Prediction
Deep learning models predict drug-target binding affinities with near-experimental accuracy.
Pros & Cons
Pros
- +Closed-loop integration of wet-lab experiments with AI models continuously improves prediction accuracy
- +Proprietary biological datasets spanning petabytes of experimental data enable novel target discovery
- +AI-powered virtual screening accelerates hit identification by 10-100x compared to traditional high-throughput screening
- +Strategic pharma partnerships validate platform capabilities with billion-dollar deal values
- +Reduces preclinical development timelines from years to months with computational candidate optimization
- +Foundation models trained on billions of molecular interactions predict drug-target binding with high accuracy
- +Multi-target drug discovery platform identifies candidates across oncology, rare diseases, and infectious disease
Cons
- −Requires substantial proprietary training data to achieve meaningful prediction accuracy improvement
- −Enterprise pricing accessible only to large pharma — prohibitive for academic labs and small biotechs
- −Long sales cycles and custom integration requirements extend time to value for new customers
- −Black-box nature of deep learning models creates interpretability challenges for regulatory submissions
Use Cases
Virtual Screening & Hit Identification
AI-powered virtual screening of billion-scale compound libraries to identify drug candidates in days instead of months.
Target Identification & Validation
Machine learning models identify novel drug targets from multi-omics data and validate their therapeutic potential.
Lead Optimization
Computational optimization of lead compounds for potency, selectivity, ADMET properties, and synthesizability.