首个高阶药物相互作用数据集，助力复杂用药安全研究
HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance
- 从FDA报告中构建多药交互数据，覆盖超千种副作用
- 简单模型表现优于图模型，超图模型更擅长捕捉复杂交互
- 适合药物安全、个性化医疗及计算药警领域研究者使用
药物不良反应研究对理解多药联合治疗中的副作用至关重要。然而，现有资源如TWOSIDES主要关注双药相互作用，缺乏更高阶的数据支持。为此，我们提出HODDI，首个高阶药物-药物相互作用数据集，基于过去十年美国食品药品监督管理局（FDA）不良事件报告系统（FAERS）数据构建。HODDI包含109,744条记录，涉及2,506种独特药物和4,569种独特不良反应，专门设计用于捕捉多药联用及其对不良反应的集体影响。全面统计分析表明，该数据集覆盖广泛且具备强分析能力。多模型评估显示，简单多层感知机（MLP）可超越图模型表现，而超图模型在捕捉复杂多药交互方面更具优势，进一步验证了其有效性。研究强调高阶信息在药物-副作用预测中的核心价值，确立了HODDI作为药警、药物安全与精准医学研究的基准数据集。数据与代码已公开于https://github.com/TIML-Group/HODDI。
原文摘要 · Abstract (English)
Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise interactions. To fill this critical gap, we introduce HODDI, the first Higher-Order Drug-Drug Interaction Dataset, constructed from U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) records spanning the past decade, to advance computational pharmacovigilance. HODDI contains 109,744 records involving 2,506 unique drugs and 4,569 unique side effects, specifically curated to capture multi-drug interactions and their collective impact on adverse effects. Comprehensive statistical analyses demonstrate HODDI's extensive coverage and robust analytical metrics, making it a valuable resource for studying higher-order drug relationships. Evaluating HODDI with multiple models, we found that simple Multi-Layer Perceptron (MLP) can outperform graph models, while hypergraph models demonstrate superior performance in capturing complex multi-drug interactions, further validating HODDI's effectiveness. Our findings highlight the inherent value of higher-order information in drug-side effect prediction and position HODDI as a benchmark dataset for advancing research in pharmacovigilance, drug safety, and personalized medicine. The dataset and codes are available at https://github.com/TIML-Group/HODDI.
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