{
  "abstract": "Prodrug design enhances critical drug properties by creating drug derivatives with improved properties while harnessing metabolic activation to regenerate the active pharmaceutical ingredient (API) in vivo . However, reliably predicting whether a prodrug converts to the intended API remains challenging, causing project failures and limiting systematic exploration of prodrug design spaces. Here, we developed PCAN-PIE (Prodrug CAndidate Navigator using a Paired-Input Ensemble), a machine learning tool to de-risk prodrug design by distinguishing prodrug candidates likely to release their intended API from those unlikely to activate. PCAN-PIE demonstrates strong performance across different approved prodrug activation chemistries, can identify competing metabolic pathways that may compromise activation, and correctly triages chemically plausible but unreported transformation products. Attribution analysis suggests that PCAN-PIE bases its predictions on chemically meaningful reactive substructures, providing medicinal chemists with an interpretable rationale for candidate prioritization. In a prospective four-candidate prodrug design campaign, PCAN-PIE successfully prioritized the two candidates that ultimately demonstrated measurable API formation and cellular activity, while deprioritizing two candidates that showed negligible conversion and activity. Taken together, this work establishes PCAN-PIE as a focused computational tool for prioritizing prodrug candidates and provides a framework for integrating structure-based prediction into rational prodrug design to support the development of safer and more effective medicines.",
  "abstract_shown": true,
  "affiliations": [
    "Duke University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chloe Markey",
      "orcid": "0009-0006-4176-3145"
    },
    {
      "affs": [
        0
      ],
      "name": "Zachary Fralish",
      "orcid": "0000-0001-6293-1730"
    },
    {
      "affs": [
        0
      ],
      "name": "Hannah Lee",
      "orcid": "0009-0009-4527-5861"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniel Reker",
      "orcid": "0000-0003-4789-7380"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d2ghx287r80wa.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv-2025-pn8qg/v3",
  "fulltext_url": null,
  "id": "d2ghx287r80wa",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-pn8qg/v3",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7223705572",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv-2025-pn8qg/v3",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/d2ghx287r80wa.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv-2025-pn8qg",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-pn8qg/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "PCAN-PIE: A Machine Learning Tool for Prioritizing Prodrug Candidates by Predicted Bioactivation",
  "title_text": "PCAN-PIE: A Machine Learning Tool for Prioritizing Prodrug Candidates by Predicted Bioactivation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/d2ghx287r80wa",
  "venue": "ChemRxiv",
  "versions": []
}