ArticleNature communications2025
Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Computational Strategy for Identifying Self-Assembling Food-Derived Molecules for Antiparasitic Nanotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Few-shot molecular property optimizationChemical science · 2026Article
- Learned Conformational Space and Pharmacophore Into Molecular Foundational Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- Task-specific pre-training for molecular property prediction.Briefings in bioinformatics · 2026Article
- Multicomplex Pharmacophore Modeling of Estrogen Receptors Suggests the Probable Repurposing of Procaterol as an Antiproliferative Agent Against Breast Cancer Cells.International journal of molecular sciences · 2026Article
- Graph neural networks driven acceleration in drug discovery.Acta pharmaceutica Sinica. B · 2025Review
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Target fishing: from "needle in haystack" to "precise guidance"--new technology, new strategy and new opportunity.Frontiers in pharmacology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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