3D-EDiffMG:3D equivariant diffusion-driven molecular generation to accelerate drug discovery
10.1016/j.jpha.2025.101257
- Author:
Chao XU
1
;
Runduo LIU
;
Yufen YAO
;
Wanyi HUANG
;
Zhe LI
;
Hai-Bin LUO
Author Information
1. Key Laboratory of Tropical Biological Resources of Ministry of Education,School of Pharmaceutical Sciences,Hainan University,Haikou,570228,China
- Publication Type:Journal Article
- Keywords:
Molecule generate;
Drug discovery;
Lead structure optimization;
Deep molecular diffusion generative model;
Dual equivariant encoder
- From:
Journal of Pharmaceutical Analysis
2025;15(6):1344-1353
- CountryChina
- Language:English
-
Abstract:
Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.
