RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design

作者：Mingyang Wang, Chang‐Yu Hsieh, Jike Wang, Dong Wang, Gaoqi Weng, Chao Shen, Xiaojun Yao, Zhitong Bing, Honglin Li, Dongsheng Cao, Tingjun Hou · 发表于：Journal of Medicinal Chemistry · 年份：2022 · DOI：10.1021/acs.jmedchem.2c00732 · 被引用次数：100 · 研究领域：Computational Drug Discovery Methods、Cancer therapeutics and mechanisms、Chemical Synthesis and Analysis

Deep learning (DL)-based de novo molecular design has recently gained considerable traction. Many DL-based generative models have been successfully developed to design novel molecules, but most of them are ligand-centric and the role of the 3D geometries of target binding pockets in molecular generation has not been well-exploited. Here, we proposed a new 3D-based generative model called RELATION. In the RELATION model, the BiTL algorithm was specifically designed to extract and transfer the desired geometric features of the protein-ligand complexes to a latent space for generation. The pharmacophore conditioning and docking-based Bayesian sampling were applied to efficiently navigate the vast chemical space for the design of molecules with desired geometric properties and pharmacophore features. As a proof of concept, the RELATION model was used to design inhibitors for two targets, AKT1 and CDK2. The calculation results demonstrated that the RELATION model could efficiently generate novel molecules with favorable binding affinity and pharmacophore features.