时间：2026-04-12
本文（Pharmacophore-oriented 3D molecular generation toward efficient feature-customized drug discovery）原载Nature Computational Science，由四川大学李国菠教授等科研人员创作，系四川智慧社会智能治理重点实验室智慧法治研究所的系列学术成果。本网站后续将持续分享实验室的系列学术成果，欢迎大家阅读。
Molecular generation is a cutting-edge technology with the potential to revolutionize intelligent drug discovery. However, currently reported ligand-based or structure-based molecular generation methods remain unpractical for real-world drug discovery. Here we propose an explicit pharmacophore-oriented 3D molecular generation method, termed PhoreGen. PhoreGen employs asynchronous perturbations and updates on both atomic and bond information, coupled with a message-passing mechanism that incorporates prior knowledge of ligand–pharmacophore mapping during the diffusion–denoising process. Evaluations revealed that PhoreGen efficiently generates 3D molecules well aligned with pharmacophores, maintaining good chemical reasonability, diversity, drug-likeness and binding affinity and, importantly, produces feature-customized molecules at high frequency. By using PhoreGen, we successfully identified new bicyclic boronate inhibitors of evolved metallo-β-lactamase and serine-β-lactamases, which potentiate meropenem against clinically isolated superbugs. Moreover, we identified inhibitors of metallo-nicotinamidases, emerging targets for insecticides. This work explores an explicitly constrained mode for molecular generation and demonstrates its potential in feature-customized drug discovery.
Peng, J.; Yu, J.-L.; Yang, Z.-B.; Chen, Y.-T.; Wei, S.-Q.; Meng, F.-B.; Wang, Y.-G.; Huang, X.-T.; Li, G.-B. Pharmacophore-oriented 3D molecular generation toward efficient feature-customized drug discovery. Nature Computational Science 2025, 5(10): 898-914.（论文下载）
