A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design

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. This paper systematically reviews the recent progress of geometric deep learning for structure-based drug design. We briefly discuss cornerstone 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, evaluation metrics, and benchmarking performance. Finally, we conclude this survey with the current challenges and highlight potential opportunities of geometric deep learning for structure-based drug design. We curate a repository of state-of-the-art studies in SBDD at \url{https://github.com/zaixizhang/Awesome-SBDD}.