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

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.

SUBMITTER: Huang L 

PROVIDER: S-EPMC10965937 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature