{
  "abstract": "Machine-learning QSAR models can prioritize antioxidant compounds, but apparent accuracy depends strongly on how chemical data are partitioned for validation. This study develops a reproducible DPPH antioxidant QSAR workflow using 1,891 curated molecules from a public 30 min DPPH dataset. Molecular structures were represented by nine RDKit physicochemical descriptors and 2,048-bit Morgan fingerprints (radius 2), giving 2,057 combined features.Random Forest regression was evaluated as the primary model,with Extra Trees as an independent ensemble comparator.Random Forest achieved mean R-squared = 0.712 and MAE = 0.297 pIC50 units under random five-fold cross-validation, but performance decreased to R-squared = 0.447 and MAE = 0.433 under Bemis-Murcko scaffold-aware validation, and to R-squared = 0.124 and MAE = 0.545 under DOI-grouped validation. In an internal held-out set, molecules with scaffolds represented during training performed better than those with unseen scaffolds(R-squared=0.824 versus 0.580).Nearest-neighbour Morgan Tanimoto analysis further separated structurally supported predictions from extrapolation: inside-domain molecules achieved R-squared = 0.830, whereas outside-domain performance was R- squared =-0.031. The workflow was then applied to two experimentally investigated sulfonamide-Tyr-Gly derivatives. Both had low maximum Tanimoto similarity(0.364 and 0.333), unseen scaffolds,and were overpredicted by approximately one pIC50 unit.The results show that random-split performance alone is insufficient for judging DPPH-QSAR reliability and that chemically informed validation and applicability-domain analysis are necessary when predictions are extended to structurally novel compounds.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Industrial Chemistry, Faculty of Applied Physical Sciences",
    "Enugu State University of Science and Technology (ESUT"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Dennis Obinna Orji",
      "orcid": "0009-0009-1674-0883"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ike Ozoemena Christian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ymfxkt9n50z5.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008549/v1",
  "fulltext_url": null,
  "id": "4ymfxkt9n50z5",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008549/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": "W7212043616",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008549/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4ymfxkt9n50z5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008549",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008549/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Reliable Machine Learning for DPPH Antioxidant QSAR: Scaffold- and Study-Aware Validation with an Out-of-Domain Sulfonamide-Tyr-Gly Case Study",
  "title_text": "Reliable Machine Learning for DPPH Antioxidant QSAR: Scaffold- and Study-Aware Validation with an Out-of-Domain Sulfonamide-Tyr-Gly Case Study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/4ymfxkt9n50z5",
  "venue": "ChemRxiv",
  "versions": []
}