综述图神经网络在药物发现中的最新进展
Recent Developments in GNNs for Drug Discovery
- 梳理分子图表示与GNN模型分类体系
- 涵盖分子生成、属性预测与药物相互作用任务
- 适合药物研发与AI交叉领域研究者参考
本文综述了图神经网络（GNNs）在计算药物发现中的最新发展，涵盖分子生成、分子属性预测和药物-药物相互作用预测。通过总结该领域的最新进展，强调GNN在理解复杂分子模式方面的能力，并探讨其当前与潜在应用。文章首先分析多种分子表示方法，随后根据输入类型和下游任务对现有GNN模型进行详细讨论与分类。同时整理了各类应用的常用基准数据集。最后对研究趋势进行简要讨论并总结。
原文摘要 · Abstract (English)
In this paper, we review recent developments and the role of Graph Neural Networks (GNNs) in computational drug discovery, including molecule generation, molecular property prediction, and drug-drug interaction prediction. By summarizing the most recent developments in this area, we underscore the capabilities of GNNs to comprehend intricate molecular patterns, while exploring both their current and prospective applications. We initiate our discussion by examining various molecular representations, followed by detailed discussions and categorization of existing GNN models based on their input types and downstream application tasks. We also collect a list of commonly used benchmark datasets for a variety of applications. We conclude the paper with brief discussions and summarize common trends in this important research area.
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