3D-EDiffMG: 3D equivariant diffusion-driven molecular generation to accelerate drug discovery
Chao Xu, Runduo Liu, Yufen Yao, Wanyi Huang, Zhe Li, Hai‐Bin Luo · Journal of Pharmaceutical Analysis · 2025
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, 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. Additionally, 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. • 3D-EDiffMG model enables scaffold-based molecular generation and lead structure optimization. • Designed 3D-SWLEE to model atomic forces in molecules, capturing covalent and non-bonded interactions. • 3D-SWLEE uses bond lengths and angles to differentiate covalent and non-bonded interactions. • 3D-SWLEE employs a gated MLP block with tiny attention to capture long-range atomic dependencies.