PharmacoMatch: Efficient 3D Pharmacophore Screening through Neural Subgraph Matching

• URL: http://arxiv.org/abs/2409.06316v1
• Date: Tue, 10 Sep 2024 08:17:06 GMT
• Title: PharmacoMatch: Efficient 3D Pharmacophore Screening through Neural Subgraph Matching
• Authors: Daniel Rose, Oliver Wieder, Thomas Seidel, Thierry Langer,
• Abstract summary: We introduce PharmacoMatch, a novel contrastive learning approach based on neural subgraph matching. Our findings demonstrate significantly shorter runtimes for pharmacophore matching, offering a promising speed-up for screening very large datasets.
• Score: 0.5113447003407372
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing size of screening libraries poses a significant challenge for the development of virtual screening methods for drug discovery, necessitating a re-evaluation of traditional approaches in the era of big data. Although 3D pharmacophore screening remains a prevalent technique, its application to very large datasets is limited by the computational cost associated with matching query pharmacophores to database ligands. In this study, we introduce PharmacoMatch, a novel contrastive learning approach based on neural subgraph matching. Our method reinterprets pharmacophore screening as an approximate subgraph matching problem and enables efficient querying of conformational databases by encoding query-target relationships in the embedding space. We conduct comprehensive evaluations of the learned representations and benchmark our method on virtual screening datasets in a zero-shot setting. Our findings demonstrate significantly shorter runtimes for pharmacophore matching, offering a promising speed-up for screening very large datasets.

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