Features
AI-predicted AAV capsid design with novel tropisms
Promoter optimization for cell-type specificity
Guide RNA design for CRISPR-based therapies
Toxicity prediction for molecular constructs
Delivery vehicle optimization (AAV, LV, non-viral)
Collaborative project workspaces
Access to proprietary biological datasets
Integration with common lab data formats
In silico construct validation before synthesis
Off-target prediction for gene editing
Target receptor identification for viral vectors
Payload design for gene therapies
Bioproduction optimization for AAV and LV
Cell therapy (CAR design) support
Multi-modal design (AAV, lentivirus, nanoparticles)
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
