A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Lei Huang, Tingyang Xu, Yang Yu, Peilin Zhao, Xingjian Chen, Jing Han, Zhi Xie, Hailong Li (), Wenge Zhong, Ka-Chun Wong () and Hengtong Zhang ()
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Lei Huang: City University of Hong Kong
Tingyang Xu: Tencent AI Lab
Yang Yu: Tencent AI Lab
Peilin Zhao: Tencent AI Lab
Xingjian Chen: Harvard Medical School
Jing Han: Regor Therapeutics Group
Zhi Xie: Regor Therapeutics Group
Hailong Li: Regor Therapeutics Group
Wenge Zhong: Regor Therapeutics Group
Ka-Chun Wong: City University of Hong Kong
Hengtong Zhang: Tencent AI Lab

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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 (Mpro) 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.

Date: 2024
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DOI: 10.1038/s41467-024-46569-1

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