MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs
- 1Omic Inc.
Abstract
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 an overall AUROC of 0.770 (0.765 for efficacy and 0.784 for safety) under structure-holdout evaluation, outperforming two published methods retrained on the same trials and folds. Scored by a mechanism-fit profiling, MERIT ranked the approved indication above the failed one for 83% (50 of 60) of a preselected set of 60 cohort compounds that failed one indication but are approved for another. Finally, we registered locked, outcome-blind predictions for 55 drug-indication pairs in ongoing phase III trials for prospective evaluation.
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BibTeX
@misc{kohtan2026merit,
title = {{MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs}},
author = {H. H. C. Koh-Tan and I. Meic and B. A. Sarı and S. Muller and G. Richman},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.09.743088},
url = {https://www.biorxiv.org/content/10.64898/2026.08.09.743088v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs
AU - H. H. C. Koh-Tan
AU - I. Meic
AU - B. A. Sarı
AU - S. Muller
AU - G. Richman
PY - 2026
DA - 2026/08/09
PB - bioRxiv
DO - 10.64898/2026.08.09.743088
UR - https://www.biorxiv.org/content/10.64898/2026.08.09.743088v2
AB - 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 an overall AUROC of 0.770 (0.765 for efficacy and 0.784 for safety) under structure-holdout evaluation, outperforming two published methods retrained on the same trials and folds. Scored by a mechanism-fit profiling, MERIT ranked the approved indication above the failed one for 83% (50 of 60) of a preselected set of 60 cohort compounds that failed one indication but are approved for another. Finally, we registered locked, outcome-blind predictions for 55 drug-indication pairs in ongoing phase III trials for prospective evaluation.
ER -