用异构图神经网络预测药物相互作用，提升临床用药安全性。
Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
- 构建异构生物医学图，融合药物、靶点等多源数据
- 在基准数据集上准确率超越现有模型，泛化能力强
- 适合药物研发与精准医疗领域的研究人员参考
药物-药物相互作用（DDIs）是临床实践中的重大问题，可能导致疗效降低或严重不良反应。传统计算方法难以捕捉药物、靶点与生物实体之间的复杂关系。本文提出HGNN-DDI，一种用于预测潜在DDI的异构图神经网络模型，通过图表示学习建模异质生物医学网络，实现跨多种节点和边类型的高效信息传播。在基准DDI数据集上的实验结果表明，HGNN-DDI在预测准确性和鲁棒性方面均优于现有先进模型，展现出支持更安全药物开发和精准医疗的巨大潜力。
原文摘要 · Abstract (English)
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to model heterogeneous biomedical networks, enabling effective information propagation across diverse node and edge types. Experimental results on benchmark DDI datasets demonstrate that HGNN-DDI outperforms state-of-the-art baselines in prediction accuracy and robustness, highlighting its potential to support safer drug development and precision medicine.
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