{
  "abstract": "Physiologically based pharmacokinetic (PBPK) models provide a mechanistic framework for drug disposition, but compound-specific parameterization remains a practical constraint when experimental absorption, distribution, metabolism, and excretion (ADME) measurements are unavailable. Sisyphus is an open-source platform that compiles ordinary differential equations from a typed physiological graph and uses molecular structure plus dose to generate oral maximum plasma concentration (Cmax) predictions. Seven fitted models support ADME estimation and a four-track geometric blend of mechanistic, direct machine-learning, clearance-based, and distribution-based predictions. This revision reconciles the manuscript with public training snapshots and a source audit of clinical reference records. Of 107 repeatedly accessed development compounds, 73 retain evaluable reference Cmax values. The hybrid prediction has a geometric mean absolute fold error (AAFE) of 2.830 (conditional bootstrap 95% confidence interval 2.30-3.52), with 43.8% of predictions within twofold; engine-only and direct machine-learning AAFE are 3.872 and 3.049. These data do not establish independent generalization or superiority of the hybrid. The user-facing residual band is development-derived and is not valid split-conformal calibration. A selected ten-compound measured-input diagnostic lowers engine AAFE from 6.708 to 5.968, while new same-model synthetic experiments illustrate Bayesian updating with explicit effective-sample-size and interval diagnostics. Neither experiment establishes clinical accuracy. A previously reported temporal cohort has been consumed by repeated analysis, and no untouched external performance estimate is available. The platform's contribution is the integration of topology-compiled simulation, structure-driven parameterization, and experimental observation-informed refinement, together with an auditable account of data provenance and limitations. Negative development experiments identify unsuccessful tested strategies; they do not demonstrate an intrinsic structure-only accuracy ceiling or a verified bioavailability-specific cause of error.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Jae Min Yoon",
      "orcid": "0009-0008-3184-929X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6kjnv3f1wgcx0.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15004452/v5",
  "fulltext_url": null,
  "id": "6kjnv3f1wgcx0",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15004452/v5",
  "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": "W7220679158",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15004452/v5",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/6kjnv3f1wgcx0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15004452",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15004452/v5"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Sisyphus: A Topology-Compiled Physiologically Based Pharmacokinetic Platform with Structure-Only Input and Bayesian Parameter Refinement",
  "title_text": "Sisyphus: A Topology-Compiled Physiologically Based Pharmacokinetic Platform with Structure-Only Input and Bayesian Parameter Refinement",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/6kjnv3f1wgcx0",
  "venue": "ChemRxiv",
  "versions": []
}