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，正逐渐成为药物发现中至关重要的环节。然而，基于物理化学建模和专家领域知识的传统方法耗时且费力。近年来，几何深度学习（集成并处理三维几何数据）的突破性进展，以及AlphaFold等工具提供的精确蛋白质三维结构预测，极大地推动了结构药物设计领域的发展。本文系统综述了几何深度学习在结构药物设计中的最新进展。我们首先简要讨论了结构药物设计中的主流任务、常用三维蛋白质表示方法及代表性预测/生成模型。随后，我们深入剖析了每个具体任务（结合位点预测、结合构象生成、从头分子生成、连接体设计及结合亲和力预测）的问题构建、代表性方法、数据集与评估指标。最后，我们总结当前面临的挑战，并指出几何深度学习在结构药物设计中潜在的研究机遇。