Hu Qiaoyu, Sun Changzhi, He Huan, Xu Jiazheng, Liu Danlin, Zhang Wenqing, Shi Sumeng, Zhang Kai, Li Honglin
Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai, China.
Lingang Laboratory, Shanghai, China.
Nat Commun. 2025 Aug 25;16(1):7928. doi: 10.1038/s41467-025-63245-0.
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.
最近用于基于结构的药物设计（SBDD）的分子生成模型，由于忽略了结构可行性和类药物性质，常常生成不切实际的三维分子。在本文中，我们引入了DiffGui，这是一种目标条件下的E(3)等变扩散模型，它整合了键扩散和性质引导，以应对上述挑战。原子扩散和键扩散的结合通过明确建模它们的相互依赖性，保证了原子和键的同时生成。性质引导将分子的结合亲和力和类药物性质纳入训练和采样过程。大量实验证明，DiffGui在生成具有高结合亲和力、合理化学结构和理想性质的分子方面优于现有方法。消融研究证实了键扩散和性质引导模块的重要性。DiffGui在从头药物设计和先导优化方面都证明了有效性，并通过湿实验室实验进行了验证。
