{
  "abstract": "Drug-drug interactions (DDIs) represent a critical challenge in drug development and clinical practice, as they can lead to severe adverse effects, including toxicity and reduced therapeutic efficacy. Deep learning methods have shown promise in large-scale, rapid DDI prediction; however, current approaches suffer from significant limitations in providing mechanistic insights into these interactions. Here, we propose DualTopoDDI, a dual-topology-enhanced interpretable deep learning model for DDI prediction. We applied DualTopoDDI to predict 9.2 billion potential interactions among approved drugs, achieving 97.99% high-confidence predictions. The model demonstrates molecular-level interpretability, identifying key substructures responsible for drug actions in terms of both atom-centric and bond-centric views. DualTopoDDI also excels in elucidating particular DDI toxicity mechanisms, which is validated by its successful explanation of the controversial cardiac toxicity in two COVID-19 drug combination regimens at the time. Evaluations across 11 benchmark datasets demonstrates that DualTopoDDI not only achieves state-of-the-art performance but also showcases robust generalizability and good interpretability. Overall, DualTopoDDI offers a powerful, interpretable tool for understanding and predicting drug-drug interactions, providing critical insights for drug safety and design.",
  "abstract_shown": true,
  "affiliations": [
    "Ocean University of China"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "W. Ma"
    },
    {
      "name": "X. Bi"
    },
    {
      "name": "H. Jiang"
    },
    {
      "name": "W. Lu"
    },
    {
      "name": "J. Nie"
    },
    {
      "name": "S. Lin"
    },
    {
      "name": "J. Lin"
    },
    {
      "name": "Z. Wei"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5bsh5a4rz163h.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.23.753684",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753684v1.full",
  "id": "5bsh5a4rz163h",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753684v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7214290015",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753684v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5bsh5a4rz163h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:28Z",
      "native_id": "10.64898/2026.09.23.753684",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753684v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Deciphering Mechanistic Signatures in Drug-Drug Interactions with Dual Topology Graphs",
  "title_text": "Deciphering Mechanistic Signatures in Drug-Drug Interactions with Dual Topology Graphs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:28Z",
  "url": "https://research.draconitic.com/paper/5bsh5a4rz163h",
  "venue": "bioRxiv",
  "versions": []
}