{
  "abstract": "Mechanistic molecular representations are increasingly used to supplement parent structure with protonation, metabolism, and quantum-chemical reactivity, but added physical detail does not necessarily provide predictive information beyond structure. StateFlux represents each drug as a network of chemical and predicted metabolic states with node-level GFN2-xTB properties and edge-resolved changes in ionization potential, electron affinity, electrophilicity, solvation, and local reactivity. On a locked DILIrank 2.0 development set (n = 376; 156 positive and 220 negative), 10 × 5 scaffold-grouped validation gave AUROC/AUPRC values of 0.8249/0.7727 for ECFP, 0.8049/0.7430 for ECFP + FluxCore, and 0.8010/0.7261 for ECFP + StateFluxCore. Strict temporal and scaffold-novel tests showed no consistent gain, and an independent 5,367-compound genotoxicity transfer benchmark reproduced the pattern (AUROC 0.7765 for ECFP versus 0.7732 for StateFluxCore). To separate limited predictive utility from inaccurate low-cost quantum chemistry, 24 parent-to-metabolite transformations were evaluated with 144 ωB97X-D/6-31+G*/SMD single-point calculations. GFN2-xTB closely tracked DFT changes in electrophilicity (Pearson r = 0.963; MAE = 0.408 eV; 79.2% sign agreement). Thus, transformation-induced quantum reactivity can be consistent with higher-level DFT without providing reliable incremental information for broad toxicity classification. Quantum-chemical fidelity and predictive utility should therefore be evaluated as separate claims in chemical machine learning.",
  "abstract_shown": true,
  "affiliations": [
    "Stanford University, 415 Broadway, Redwood City, California, 94063"
  ],
  "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/5q7cvqp9xn02c.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009594/v1",
  "fulltext_url": null,
  "id": "5q7cvqp9xn02c",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009594/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": "W7214188939",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009594/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/5q7cvqp9xn02c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009594",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009594/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "StateFlux: DFT-Validated Quantum Reactivity across Predicted Metabolic State Networks Shows Limited Incremental Value for Broad Toxicity Prediction",
  "title_text": "StateFlux: DFT-Validated Quantum Reactivity across Predicted Metabolic State Networks Shows Limited Incremental Value for Broad Toxicity Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/5q7cvqp9xn02c",
  "venue": "ChemRxiv",
  "versions": []
}