论文丨Yu, J.-L.; Zhou, C.; Ning, X.-L.; Mou, J.; Meng, F.-B.; Wu, J.-W.; Chen, Y.-T.; Tang, B.-D.; Liu, X.-G.; Li, G.-B： Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping

本文（Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping）原载 Nature Communications ，由四川大学李国菠教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract: Pharmacophores are abstractions of essential chemical interaction patterns, holding an irreplaceable position in drug discovery. Despite the availability of many pharmacophore tools, the adoption of deep learning for pharmacophore-guided drug discovery remains relatively rare. We herein propose a knowledge-guided diffusion framework for 'on-the-fly' 3D ligand-pharmacophore mapping, named DiffPhore. It leverages ligand-pharmacophore matching knowledge to guide ligand conformation generation, meanwhile utilizing calibrated sampling to mitigate the exposure bias of the iterative conformation search process. By training on two self-established datasets of 3D ligand-pharmacophore pairs, DiffPhore achieves state-of-the-art performance in predicting ligand binding conformations, surpassing traditional pharmacophore tools and several advanced docking methods. It also manifests superior virtual screening power for lead discovery and target fishing. Using DiffPhore, we successfully identify structurally distinct inhibitors for human glutaminyl cyclases, and their binding modes are further validated through co-crystallographic analysis. We believe this work will advance the AI-enabled pharmacophore-guided drug discovery techniques.

Yu, J.-L.; Zhou, C.; Ning, X.-L.; Mou, J.; Meng, F.-B.; Wu, J.-W.; Chen, Y.-T.; Tang, B.-D.; Liu, X.-G.; Li, G.-B. Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping. Nature Communications 2025, 16, 2269.（论文下载）