Structure-based drug design (SBDD), which utilizes the three-dimensional geometry of proteins to identify potential drug candidates, is becoming increasingly vital in drug discovery. However, traditional methods based on physiochemical modeling and experts' domain knowledge are time-consuming and laborious. The recent advancements in geometric deep learning, which integrates and processes 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have significantly propelled progress in structure-based drug design. In this paper, we systematically review the recent progress of geometric deep learning for structure-based drug design. We start with a brief discussion of the mainstream tasks in structure-based drug design, commonly used 3D protein representations and representative predictive/generative models. Then we delve into detailed reviews for each task (binding site prediction, binding pose generation, \emph{de novo} molecule generation, linker design, and binding affinity prediction), including the problem setup, representative methods, datasets, and evaluation metrics. Finally, we conclude this survey with the current challenges and highlight potential opportunities of geometric deep learning for structure-based drug design.

翻译：结构药物设计（SBDD）利用蛋白质的三维几何结构来识别潜在候选药物，在药物发现中日益重要。然而，传统基于物理化学建模和专家领域知识的方法耗时费力。近年来，几何深度学习的进展——该技术集成并处理三维几何数据，加之AlphaFold等工具能够准确预测蛋白质三维结构，极大地推动了结构药物设计的发展。本文系统回顾了基于几何深度学习的结构药物设计最新进展。我们首先简要讨论结构药物设计中的主流任务、常用三维蛋白质表示方法以及代表性的预测/生成模型。随后针对每项任务（结合位点预测、结合姿态生成、从头分子生成、连接子设计及结合亲和力预测）进行详细综述，包括问题设定、代表性方法、数据集和评估指标。最后，我们总结了当前面临的挑战，并指出了几何深度学习在结构药物设计中的潜在机遇。