{
  "abstract": "A small external evaluation was performed to examine the practical accuracy and applicability domain of two pretrained machine-learning models for solution-state NMR chemical-shift prediction in five pharmaceutically relevant small molecules: amylmetacresol, benzocaine, dopamine, betazole, and betahistine.The pretrained GT-NMR graphtransformer model was used for atom-specific 13C chemical-shift prediction, while the CASCADE 3D graph neural-network workflow was used for 1H chemical-shift prediction.Predictions were compared with primary-literature NMR data where numerical assignments could be verified.Exact dataset overlap, nearest-training-set Morgan-fingerprint Tanimoto similarity, Murcko-scaffold coverage, and molecular complexity were evaluated as applicability-domain diagnostics.None of the five exact structures was detected in the checked GT-NMR or CASCADE proton train/validation/test partitions, although all five Murcko scaffolds were represented in the corresponding training sets.For GT-NMR, audited 13C molecule-level MAEs were 0.50 ppm for benzocaine, 2.59 ppm for",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Diba Allameh Zadeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bnzjn4pdh5jf3.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008562/v1",
  "fulltext_url": null,
  "id": "bnzjn4pdh5jf3",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008562/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": "W7211994047",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008562/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/bnzjn4pdh5jf3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008562",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008562/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Applicability-Domain-Aware Evaluation of Machine-Learning NMR Chemical-Shift Prediction in Small drug Molecules: GT-NMR for 13C and CASCADE for 1H",
  "title_text": "Applicability-Domain-Aware Evaluation of Machine-Learning NMR Chemical-Shift Prediction in Small drug Molecules: GT-NMR for 13C and CASCADE for 1H",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/bnzjn4pdh5jf3",
  "venue": "ChemRxiv",
  "versions": []
}