{
  "abstract": "We report a multimodal graph-based deep learning framework that integrates molecular graph topology with physicochemical descriptors to predict the heat of combustion (HoC) directly from chemical structure. The model employs a neural network convolution (NNConv) to capture bonding and topological information, while molecular descriptors provide complementary global features. Trained on 4,516 experimental data points, the model achieved a mean absolute error (MAE) of 351.4 kJ/mol (1.89 kJ/g), corresponding to approximately 6.35% of the dataset mean (5529.65 kJ/mol), and a coefficient of determination (R²) of 0.942 on the test set. When benchmarked against linear regression, random forest, and graph-only GNN models, the corresponding MAE and R² values were 376.0 and 0.925, 381.7 and 0.914, and 1608.8 and 0.484, respectively, highlighting the benefit of integrating descriptors with graph-based learning using NNConv. The predicted HoC values enable reliable classification with respect to the 20 kJ/g GHS/CLP decision threshold, except for compounds within an ambiguity band of ± 1.89 kJ/g. Residual analysis showed low bias and an approximately normal error distribution. These results demonstrate that multimodal graph-based learning can provide decision-relevant HoC predictions, supporting chemical hazard screening while reducing reliance on experimental measurements.",
  "abstract_shown": true,
  "affiliations": [
    "Worker’s Care & Digital Health Lab, Department of Future Technology, Korea University of Technology and Education",
    "Department of Digital Anti-aging Healthcare (BK21), Inje University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
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        0
      ],
      "name": "Osita Sunday Nnyigide"
    },
    {
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      ],
      "name": "Haewon Byeon"
    },
    {
      "affs": [
        1
      ],
      "name": "Uchenna Esther Okpete"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cm9qnw6x8g0ey.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-60540-8",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-60540-8",
  "id": "cm9qnw6x8g0ey",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-60540-8",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
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      "lcc",
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  "pdf_url": "https://www.nature.com/articles/s41598-026-60540-8_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/cm9qnw6x8g0ey.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:41Z",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Multimodal graph-based deep learning for predicting heat of combustion as a key hazard indicator under GHS/CLP standards",
  "title_text": "Multimodal graph-based deep learning for predicting heat of combustion as a key hazard indicator under GHS/CLP standards",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:41Z",
  "url": "https://research.draconitic.com/paper/cm9qnw6x8g0ey",
  "venue": "Scientific Reports",
  "versions": []
}