{
  "abstract": "Drug development is an expensive and lengthy process requiring developers to select drugs for human trials and diseases to target. Computational tools that can estimate before testing which drug-disease candidates are most likely to reach FDA approval could reduce development time and costs through early program advancement and disease prioritization. Yet existing approaches predict outcomes at a single level or stage or do not distinguish between disease outcomes for the same drug. We ask (1) whether FDA approval can be forecast for a drug-disease pair using only pre-clinical trial information, and (2) whether these forecasts can help prioritize diseases for the same drug. We built a cohort of 14,134 drug-disease pairs by reconstructing programs from ClinicalTrials.gov, extracting disease-level approvals from FDA label documentation, and enriching each pair with molecular, disease, ADMET, target, and pathway features. We divided the cohort into pre- and post-2019 programs and trained a classifier on pre-2019 data to predict FDA approval. On the post-2019 cohort (2,637 programs), the model would have prioritized a program that ultimately gained approval over one that did not approximately three out of four times. For drugs tested across multiple diseases, selecting the model's highest-ranked disease would have identified an ultimately approved disease 61.3% of the time, compared with 40.7% under random selection. These results show that public pre-trial information contains signal for forecasting FDA approval at the drug-disease level and can help prioritize diseases for the same drug. We provide a public web tool for SMILES- and ICD-10-based drug-disease approval forecasting.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14013",
  "author_total": 3,
  "authors": [
    {
      "name": "Samir Townsley"
    },
    {
      "name": "Pagkratios Tagkopoulos"
    },
    {
      "name": "Ilias Tagkopoulos"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fj7e8pt8x4jy0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14013",
  "fulltext_url": null,
  "id": "fj7e8pt8x4jy0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14013",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221799280",
  "pdf_url": "https://arxiv.org/pdf/2610.14013",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fj7e8pt8x4jy0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:21Z",
      "native_id": "2610.14013",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14013"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Disease-level drug approval forecasting from trial-agnostic public data",
  "title_text": "Disease-level drug approval forecasting from trial-agnostic public data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:21Z",
  "url": "https://research.draconitic.com/paper/fj7e8pt8x4jy0",
  "venue": "arXiv",
  "versions": []
}