How Roche Leverages AI and Machine Learning to Revolutionize Drug Discovery and Development

Roche and its subsidiary Genentech use AI and machine learning (ML) to analyze vast biomedical data and simulate drug interactions, accelerating the drug discovery process. Their “lab in a loop” approach iteratively trains AI models on lab and clinical data to predict drug targets, therapeutic molecules, and optimize molecule design. Collaborations with partners like Recursion Pharmaceuticals and Genesis Therapeutics enhance capabilities in cell biology mapping, molecular simulation, and antibody design. This AI-driven strategy reduces R&D time, costs, and improves success rates in identifying promising drug candidates.

Traditional drug discovery is slow, costly, and has a high failure rate due to complex biological data and trial-and-error methods

AI/ML models trained on massive datasets from lab experiments and clinical studies; generative AI for molecule design; simulation of drug-target interactions; partnerships with AI companies for advanced computational platforms