Features
AI-driven target identification from multi-omics data
Target ranking with predicted efficacy and safety scores
Integration with public biomedical databases (ChEMBL, DisGeNET)
Custom model training on user-uploaded datasets
Collaborative workspace for research teams
Exportable reports in PDF and CSV format
Visualization of target-disease associations
Literature mining for target validation evidence
Off-target prediction and toxicity estimation
API access for automated workflows
Proprietary Recursion OS platform for drug discovery
Automated wet lab generating millions of cell experiments per week
Over 50 petabytes of biological and chemical data
NVIDIA BioHive-2 supercomputer for AI model training
Hit identification to IND-enabling studies with improved speed and cost
Potential first-in-class treatments in oncology and rare diseases
Advanced pipeline with phase 1/2/3 clinical trials (REC-4881, REC-3565)
Target identification and validation using machine learning
Generative AI for molecule design and optimization
De-identified patient data integration for precision medicine
Strategic partnerships with pharma and tech leaders
End-to-end drug development capability from preclinical to pivotal studies
Fit-for-purpose dataset generation including ADME and safety profiling
Real-world evidence generation through clinical trials
Continuous feedback loop from experiments into AI models
