{
  "abstract": "Experimental screening of large hydrogel-associated chemical collections is slow and resource-intensive. We developed an ADMET-informed machine-learning workflow for 2060 HydrogelDB-linked compounds, using the median of a separately reported admetSAR3.0 biodegradability-related output to define higher- and lower-score classes and the remaining 115 predicted ADMET properties as model inputs; preprocessing, feature selection, hyperparameter optimization, probability calibration, and threshold selection were confined to the training partitions of nested cross-validation. The calibrated linear SVM achieved a mean held-out MCC of 0.9116 ± 0.0222, an F2-score of 0.9662 ± 0.0112, and a ROC–AUC of 0.9904 ± 0.0048; performance remained high after proxy-feature removal and feature-family ablation, whereas structure-focused models showed a weaker but detectable signal. Strict eligibility criteria retained 19 candidates. Six-component ranking under changes in model outputs and weights identified D-Alanyl-D-Alanine, Prolyl-Glutamate, and Pro-Asp as the most stable leading candidates. N-Glycylalanine showed lower ranking stability, with Isoleucylglycine as the main alternative. All shortlisted compounds require experimental validation of biodegradability, hydrogel formation, and nanocarrier performance.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Zeinab Hosseinyazdi"
    },
    {
      "name": "Seyed Majid Hashemianzadeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7vc26jx5jd4s3.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009753/v1",
  "fulltext_url": null,
  "id": "7vc26jx5jd4s3",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009753/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7215119686",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009753/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/7vc26jx5jd4s3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009753",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009753/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "ADMET-Informed Machine Learning Prioritization of Hydrogel-Associated Compounds for Nanocarrier Evaluation",
  "title_text": "ADMET-Informed Machine Learning Prioritization of Hydrogel-Associated Compounds for Nanocarrier Evaluation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/7vc26jx5jd4s3",
  "venue": "ChemRxiv",
  "versions": []
}