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

Publication date: Mar 26, 2024

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) 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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Concepts Keywords
Cdk2 Based
Global Cdk2
Ligands Conditional
Outperforms Design
Vitro Diffusion


Type Source Name
disease VO efficient
disease VO USA
drug DRUGBANK Coenzyme M
disease IDO algorithm
disease VO time
disease VO inefficient
drug DRUGBANK Spinosad
disease IDO process
pathway REACTOME Translation
drug DRUGBANK Water
drug DRUGBANK Glutathione
drug DRUGBANK Pantothenic acid
disease VO Gap
disease VO viable
drug DRUGBANK Perampanel
drug DRUGBANK Nitrogen
drug DRUGBANK Trestolone
disease MESH cancers
drug DRUGBANK L-Lysine
disease IDO assay
drug DRUGBANK Activated charcoal
drug DRUGBANK Stavudine

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