Features
Single-cell RNA-seq analysis: cluster and label cell types from .h5ad
Exome variant prioritization using ClinVar, gnomAD, evidence-based scores
ADMET and drug-likeness prediction for compounds
CRISPR screen design and analysis
Literature summarization with biomarker and therapeutic target extraction
Drug repurposing candidate ranking for rare diseases
Gene regulatory network inference from multi-omic data
Rare disease diagnostic reasoning from patient phenotype and variants
Integration of 150+ specialized tools and 100+ databases
Transparent reasoning chains for reproducibility
UMAP/t-SNE visualization generation
Wet-lab protocol recommendation and design
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
