{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Omic Inc."
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. H. C. Koh-Tan"
    },
    {
      "name": "I. Meic"
    },
    {
      "name": "B. A. Sarı"
    },
    {
      "name": "S. Muller"
    },
    {
      "name": "G. Richman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ceeq4jedyff8a.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.09.743088",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.09.743088v2.full",
  "id": "ceeq4jedyff8a",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.09.743088v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7203450521",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.09.743088v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ceeq4jedyff8a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:03Z",
      "native_id": "10.64898/2026.08.09.743088",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.09.743088v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs",
  "title_text": "MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:03Z",
  "url": "https://research.draconitic.com/paper/ceeq4jedyff8a",
  "venue": "bioRxiv",
  "versions": []
}