Features
Federated learning for multi-center studies
AI-based workflow design and execution
Seamless PACS integration
Containerized processing with Kubernetes and Docker
nnU-Net integration for segmentation
MITK Workbench for interactive image viewing
Standardized interfaces for algorithm sharing
Modular and extensible architecture
Web-based user interface for platform management
Open-source with community contributions
Supports distributed method development
Built-in nnU-Net out-of-the-box segmentation
MITK Workbench for interactive medical image processing
Containerized data processing
Web-based UI for platform management
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
