{
  "abstract": "Typical submissions to the Therapeutics Data Commons (TDC) absorption, distribution, metabolism, excretion, and toxicity (ADMET) benchmark leaderboard apply a single architecture and a single feature representation across all 22 endpoints, despite substantial heterogeneity in evaluation metric, training-set size, and class balance. Rather than proposing a new architecture, we show that endpoint-specific selection of molecular representations and CatBoost hyperparameters, carried out under the official TDC ADMET benchmark protocol, is a competitive alternative to that uniform recipe. For each endpoint, a fixed CatBoost backbone was paired with configurations chosen from a common candidate set comprising published fingerprints and physicochemical descriptors, truncated three-dimensional PaDEL descriptors, frozen graph isomorphism network (GIN) and MiniMol embeddings, and a convex combination with a shallow network trained on the MiniMol embedding alone. All feature and hyperparameter choices were made on an internal scaffold validation split under a prespecified strict-improvement rule. Against a frozen snapshot of the public leaderboard, the resulting Ark-ADMET family of 22 endpoint-specific models matched or exceeded the best previously listed score on 7 of 21 rankable endpoints and strictly exceeded it on 3. All selected configurations retain the baseline fingerprint-and-descriptor block; optional components differ across endpoints. Taken together, these results show that competitive performance on this benchmark can be achieved without a new architecture or a single optional representation shared across all endpoints.",
  "abstract_shown": true,
  "affiliations": [
    "Eonark"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Y. Shi"
    },
    {
      "name": "H. Luo"
    },
    {
      "name": "F. Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/174ent229tv6f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762420",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762420v1.full",
  "id": "174ent229tv6f",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762420v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762420v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/174ent229tv6f.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:21:33Z",
      "native_id": "10.64898/2026.10.28.762420",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762420v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Ark-ADMET: endpoint-specific selection of molecular representations and hyperparameters for gradient-boosted ADMET models under the Therapeutics Data Commons benchmark protocol",
  "title_text": "Ark-ADMET: endpoint-specific selection of molecular representations and hyperparameters for gradient-boosted ADMET models under the Therapeutics Data Commons benchmark protocol",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:56Z",
  "url": "https://research.draconitic.com/paper/174ent229tv6f",
  "venue": "bioRxiv",
  "versions": []
}