Target-aware 3D molecular generation based on guided equivariant diffusion

Qiaoyu Hu (), Changzhi Sun, Huan He, Jiazheng Xu, Danlin Liu, Wenqing Zhang, Sumeng Shi, Kai Zhang and Honglin Li ()
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Qiaoyu Hu: East China Normal University
Changzhi Sun: Lingang Laboratory
Huan He: East China Normal University
Jiazheng Xu: East China Normal University
Danlin Liu: East China Normal University
Wenqing Zhang: East China Normal University
Sumeng Shi: East China Normal University
Kai Zhang: East China Normal University
Honglin Li: East China Normal University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Recent molecular generation models for structure-based drug design (SBDD) often produce unrealistic 3D molecules due to the neglect of structural feasibility and drug-like properties. In this paper, we introduce DiffGui, a target-conditioned E(3)-equivariant diffusion model that integrates bond diffusion and property guidance, to address the above challenges. The combination of atom diffusion and bond diffusion guarantees the concurrent generation of both atoms and bonds by explicitly modeling their interdependencies. Property guidance incorporates the binding affinity and drug-like properties of molecules into the training and sampling processes. Extensive experiments prove that DiffGui outperforms existing methods in generating molecules with high binding affinity, rational chemical structure, and desirable properties. Ablation studies confirm the importance of bond diffusion and property guidance modules. DiffGui demonstrates effectiveness in both de novo drug design and lead optimization, with validation through wet-lab experiments.

Date: 2025
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DOI: 10.1038/s41467-025-63245-0

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