A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets

Abstract

Structure-based generative chemistry aims to explore much bigger chemical space to design a ligand with high binding affinity to the target proteins; it is a critical step in de novo computer-aided drug discovery. Traditional in silico methods suffer from calculation inefficiency and the performances of existing machine learning methods could be bottlenecked by the auto-regressive sampling strategy. To address these concerns, we herein have developed a novel conditional deep generative model, PMDM, for 3D molecule generation fitting specified target proteins. PMDM incorporates a dual equivariant diffusion model framework to leverage the local and global molecular dynamics to generate 3D molecules in a one-shot fashion. By considering the conditioned protein semantic information and spatial information, PMDM is able to generate chemically and conformationally valid molecules which suitably fit pocket holes. We have conducted comprehensive experiments to demonstrate that PMDM can generate drug-like, synthesis-accessible, novel, and high-binding affinity molecules targeting specific proteins, outperforming the state-of-the-art (SOTA) models in terms of multiple evaluation metrics. In addition, we perform chemical space analysis for generated molecules and lead compound optimization for SARS-CoV-2 main protease (Mpro) by only utilizing three atoms as the seed fragment. The experimental results implicate that the structures of generated molecules are rational compared to the reference molecules, and PMDM can generate massive bioactive molecules highly binding to the targeted proteins which are not included in the training set.