{
  "abstract": "Drug phototoxicity depends on electronically excited states whose energies and intensities can change with protonation, tautomerism, and conformation, yet most predictive models represent a single parent structure. We evaluated a molecular-state-aware workflow on a curated 450-compound in vitro photoirritation (PIV) benchmark expanded to 1,088 microstates. Across 10 repeated 5-fold scaffold-held-out cross-validations, mean ROC-AUC increased from 0.745 for structure alone to 0.792 after adding parent xTB+sTDA descriptors, 0.807 with microstate summaries, and 0.808 with conformer aggregation; PR-AUC increased from 0.669 to 0.727. Compound-paired bootstrap analysis gave a full-model ROC-AUC gain of +0.0569 (95% CI 0.0234-0.0908) and PR-AUC gain of +0.0533 (0.0133-0.0933). Higher-level Q-Chem calculations preserved screening excited-state ordering (S1 Pearson r = 0.946; T1 r = 0.931), and an additional preselected state panel completed 22/24 TDA jobs with all four parent-alternate microstate pairs available. These pairs showed substantial state-dependent changes, including a 240.4 to 259.0 nm bright-state shift for trinexapac-ethyl. Clinical PIH endpoint transfer was modest (n = 155; ROC-AUC 0.609), consistent with endpoint and chemical-space shift. The results support molecular-state-aware excited-state featurization as a mechanistically grounded strategy for computational photosafety.",
  "abstract_shown": true,
  "affiliations": [
    "Stanford Online High School, Stanford University, 415 Broadway, Redwood City, California 94063, United States"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Aris Tsai",
      "orcid": "0009-0004-8641-7647"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0m3j9ws3rw452.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009622/v1",
  "fulltext_url": null,
  "id": "0m3j9ws3rw452",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009622/v1",
  "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": "W7214775764",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009622/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0m3j9ws3rw452.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009622",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009622/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Microstate-Aware Excited-State Descriptors Improve Scaffold-Robust Prediction of Drug Phototoxicity",
  "title_text": "Microstate-Aware Excited-State Descriptors Improve Scaffold-Robust Prediction of Drug Phototoxicity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/0m3j9ws3rw452",
  "venue": "ChemRxiv",
  "versions": []
}