计算机科学
生物信息学
化学空间
药物发现
生物系统
人工智能
计算生物学
化学
生物信息学
生物
生物化学
基因
作者
Lei Huang,Tingyang Xu,Yang Yu,Peilin Zhao,Xingjian Chen,Jing Han,Zhi Xie,Hailong Li,Wenge Zhong,Ka‐Chun Wong,Hengtong Zhang
标识
DOI:10.1038/s41467-024-46569-1
摘要
Abstract Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (M pro ) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.
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