Features
AI-powered prediction of optimal delivery parameters
Support for DNA, mRNA, siRNA, and CRISPR payloads
Coverage of over 500 cell types including primary and stem cells
Electroporation and lipid nanoparticle protocol recommendations
Viral vector design suggestions (AAV, lentivirus)
Experimental data ingestion to improve models
Collaborative workspace for teams
Integration with common lab data formats
Protocol versioning and sharing
Usage analytics dashboard
Community-driven protocol database
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
