TECHNOLOGY AND CODE article
Front. Bioinform.
Sec. Drug Discovery in Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1628800
This article is part of the Research TopicAI in Drug DiscoveryView all articles
PharmacoForge: Pharmacophore generation with diffusion models
Provisionally accepted
- University of Pittsburgh, Pittsburgh, United States
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Structure-based drug design (SBDD) is enhanced by machine learning (ML) to improve both virtual screening and de novo design. Despite advances in ML tools for both strategies, screening remains bounded by time and computational cost, while generative models frequently produce invalid and synthetically inaccessible molecules. Screening time can be improved with pharmacophore search, which quickly identifies ligands in a database that match a pharmacophore query. In this work, we introduce PharmacoForge, a diffusion model for generating 3D pharmacophores conditioned on a protein pocket. Generated pharmacophore queries identify ligands that are guaranteed to be valid, commercially available molecules. We evaluate PharmacoForge against automated pharmacophore generation methods using the LIT-PCBA benchmark and ligand generative models through a docking-based evaluation framework.We further assess pharmacophore quality through a retrospective screening of the DUD-E dataset. PharmacoForge surpasses other pharmacophore generation methods in the LIT-PCBA benchmark, and resulting ligands from pharmacophore queries performed similarly to de novo generated ligands when docking to DUD-E targets and had lower strain energies compared to de novo generated ligands.
Keywords: structure-based drug discovery, Pharmacophore, diffusion models, Virtual Screening, Generative models, Molecule generation, Computational Drug Discovery
Received: 14 May 2025; Accepted: 08 Aug 2025.
Copyright: © 2025 Flynn, Shah, Dunn, Aggarwal and Koes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: David Ryan Koes, University of Pittsburgh, Pittsburgh, United States
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