MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs

Drug development depends on efficacy and safety, but many trial-outcome prediction models incorporate trial design, prior development history or compound identity, enabling compound memorization and inflating apparent performance. We developed MEchanism-Resolved Inference of Trial outcomes (MERIT), a model that predicts trial outcomes from molecular and disease features without using information on similar-compound success. MERIT integrates the disease and drug of interest with large-scale drug-protein, protein-metabolite and immune interaction maps to link a drug's intended and potential off-target effects to tissue-specific efficacy and safety. Across 753 small-molecule drugs and 3,133 trials, MERIT achieved a best-in-class overall AUROC of 0.770 (0.765 for efficacy and 0.784 for safety). MERIT also recovered the eventual approved indications for 83% of failed drugs. Finally, we registered locked, outcome-blind predictions for 55 drug-indication pairs in ongoing Phase III trials, establishing a prospective evaluation cohort.