药物副作用预测在药物结构优化、剂量调整和个体化用药策略制定中发挥着关键作用, 是现代药物研发体系与临床用药管理的重要组成部分。基于智能计算的方法能够有效克服传统药物副作用预测方法中存在的高成本、周期长等局限。作为一种能够高效处理图结构数据的新兴深度学习模型,图神经网络(graph neural network, GNN)能够捕获药物分子结构特征与生物系统之间的复杂关联, 可有效提升药物副作用预测模型的准确性与泛化能力。本文对图神经网络在药物副作用预测领域的研究进展作一综述, 并对其未来的发展趋势进行展望。

Drug side effect prediction plays a crucial role in molecular structure optimization, dose adjustment, and the development of personalized medication strategies, representing an essential component of modern drug research pipelines and clinical medication management. Intelligent computational methods can effectively overcome the limitations inherent in traditional prediction approaches, such as high costs and prolonged development cycles. As an emerging deep learning paradigm capable of effectively modeling graph-structured data, graph neural networks (GNNs) can capture complex relationships between drug molecular structures and biological systems, thereby enhancing the accuracy and generalizability of side-effect prediction models. This article provides a comprehensive review of recent advances in GNN-based drug side effect prediction and discusses potential future research directions in this field.