Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping

Jun-Lin Yu, Cong Zhou, Xiang-Li Ning, Jun Mou, Fan-Bo Meng, Jing-Wei Wu, Yi-Ting Chen, Biao-Dan Tang, Xiang-Gen Liu () and Guo-Bo Li ()
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Jun-Lin Yu: Sichuan University
Cong Zhou: Sichuan University
Xiang-Li Ning: Sichuan University
Jun Mou: Sichuan University
Fan-Bo Meng: Sichuan University
Jing-Wei Wu: Sichuan University
Yi-Ting Chen: Sichuan University
Biao-Dan Tang: Sichuan University
Xiang-Gen Liu: Sichuan University
Guo-Bo Li: Sichuan University

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

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-57485-3

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