School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, Gansu, PR China.
Tencent AI Lab, Shenzhen 518000, PR China.
Drug Discov Today. 2024 Jul;29(7):104024. doi: 10.1016/j.drudis.2024.104024. Epub 2024 May 16.
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.
基于 3D 结构的药物设计（SBDD）被认为是一种具有挑战性和合理性的创新药物发现方法。几何深度学习是一种很有前途的方法，它通过构建神经网络模型来学习非欧几里得数据，如 3D 分子图和流形数据，从而解决了 3D SBDD 的精确模型训练问题。在这里，我们总结了包含 3D 分子表示、等变图神经网络（EGNN）和六种生成模型方法（扩散模型、基于流的模型、生成对抗网络（GAN）、变分自编码器（VAE）、自回归模型和基于能量的模型）的几何深度学习方法及其应用。我们的综述提供了对 3D SBDD 的几何深度学习方法和高级应用的深入了解，这将对药物发现社区具有重要意义。
