Geometric deep learning methods and applications in 3D structure-based drug design

•How do the deep learning methods deal with 3D SBDD?•3D molecular representations include 3D surface, 3D grid and 3D graph.•EGNNs can handle the high-dimensional information data.•Six generative models can produce various potential data for 3D SBDD. 3D structure-based drug design (SBDD) is considered a challenging and rational way for innovative drug discovery. Geometric deep learning is a promising approach that solves the accurate model training of 3D SBDD through building neural network models to learn non-Euclidean data, such as 3D molecular graphs and manifold data. Here, we summarize geometric deep learning methods and applications that contain 3D molecular representations, equivariant graph neural networks (EGNNs), and six generative model methods [diffusion model, flow-based model, generative adversarial networks (GANs), variational autoencoder (VAE), autoregressive models, and energy-based models]. Our review provides insights into geometric deep learning methods and advanced applications of 3D SBDD that will be of relevance for the drug discovery community.