3D Molecule Generation via Diffusion Model with a Self-Attention-Based EGNN
Huanai Yang, Qi Zhong, Min Wang, Jingqing Peng, Dingcai Shen · ACS Omega · 2025
High Resolution Image Download MS PowerPoint Slide The discovery of new drugs is of great significance to human health. The diffusion model has emerged as a powerful tool for generating 3D molecular structures, achieving significant success in various applications. However, existing models lack direct interatomic confinement features and are difficult to capture long-range dependencies in macromolecules. These issues lead to the generation of molecular structures that are either inaccurate or lack rationality. To address the above issues, this paper proposes a novel 3D Molecule Generation via Diffusion Model with a self-attention-based E( n ) equivariant graph neural network (EGNN) named MGDM-Sa, which incorporates a dual equivariant score neural network DualESNet, to capture the global graph features and the local atomic environment. Especially, in DualESNet, we design a novel equivariant encoder eEncoder, which integrates EGNN and self-attention. During message propagation, EGNN ensures the equivariation of geometric features, and self-attention layer captures the long-range dependencies in the molecular graph. Thus, this design enhances the representation capability of MGDM-Sa. The experimental results demonstrate that MGDM-Sa is capable of generating valid, unique, novel, stable, and diverse drug-like molecules, thereby underscoring its potential to accelerate the drug discovery process.