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

Publisher: Elsevier Science Ltd. Country of Publication: England NLM ID: 9604391 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-5832 (Electronic) Linking ISSN: 13596446 NLM ISO Abbreviation: Drug Discov Today Subsets: MEDLINE

Original Publication: Kidlington, Oxford : Irvington, NJ : Elsevier Science Ltd. ; Distributed by Virgin Mailing and Distribution, c1996-

Deep Learning* ; Drug Design* ; Neural Networks, Computer*; Drug Discovery/methods ; Humans ; Molecular Structure

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.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)

Keywords: 3D structure-based drug design; deep learning; generative models; geometric deep learning

Date Created: 20240517 Date Completed: 20240706 Latest Revision: 20240706

20240707

10.1016/j.drudis.2024.104024

38759948