{
  "abstract": "Stevens-Johnson syndrome (SJS) is a rare, life-threatening adverse drug reaction for which early identification of high-risk drugs remains challenging. In this study, we developed an explainable quantitative structure-toxicity relationship (QSTR) framework for SJS prediction using a multi-view learning framework with 21 molecular representations, including molecular descriptors, fingerprints, transformer-based embeddings, and a molecular similarity representation. Model development and evaluation were designed in accordance with the OECD principles for QSAR validation, incorporating scaffold-aware validation, external evaluation on pharmacovigilance negative-signal compounds, and structural interpretation by integrating SHAP with Bemis-Murcko scaffold enrichment analysis. The proposed framework achieved robust sensitivity and specificity, with molecular similarity consistently improving the identification of SJS-associated compounds. Furthermore, a consensus prediction and scaffold-based prioritization strategy identified several high-risk drugs from the 1 external negative-signal dataset, including cefprozil, tetracycline, and omadacycline, highlighting their potential association with SJS and warranting further pharmacovigilance and experimental investigation. To facilitate practical application, this was deployed as a freely accessible web server, SimSJSAlert (https: //sim-sjs-alert.streamlit.app/), enabling rapid drug-induced SJS risk assessment. The proposed framework combines predictive performance, interpretability, and accessibility to support early SJS risk assessment, pharmacovigilance signal prioritization, and safer drug development.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Health Sciences, College of Natural Sciences",
    "Can Tho University",
    "Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences",
    "Khon Kaen University",
    "Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Huynh Anh Duy",
      "orcid": "0009-0008-0436-6014"
    },
    {
      "affs": [
        4,
        3
      ],
      "name": "Sastiya Kampaengsri"
    },
    {
      "affs": [
        4,
        3
      ],
      "name": "Supreeya Paiboon"
    },
    {
      "affs": [
        4,
        3
      ],
      "name": "Tarapong Srisongkram",
      "orcid": "0000-0001-8512-5379"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2sezps283zy6f.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008910/v1",
  "fulltext_url": null,
  "id": "2sezps283zy6f",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008910/v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7213425373",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008910/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/2sezps283zy6f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008910",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008910/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "SimSJSAlert: A Similarity-Augmented Multi-View Learning Framework with Scaffold Alerts for Drug-Induced Stevens-Johnson Syndrome Risk Assessment",
  "title_text": "SimSJSAlert: A Similarity-Augmented Multi-View Learning Framework with Scaffold Alerts for Drug-Induced Stevens-Johnson Syndrome Risk Assessment",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/2sezps283zy6f",
  "venue": "ChemRxiv",
  "versions": []
}