Features
Multimodal AI prediction of drug effects across multiple neural disorders
Target discovery for neurotherapeutics targeting neural circuits
Agnostic to therapeutic modality (small molecules, biologics, etc.)
Multi-target effect identification for complex disorders
Highly localized (circuit-level) targeting
Agnostic to genetic background of models
Predicts conserved neural manifolds linking animal to human outcomes
Integration of neural, behavioral, molecular, and clinical readouts
Longitudinal tracking for disease progression modeling
Neuroscience data factory in Manila for scalable multimodal data generation
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
