PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching

Daniel Rose, Oliver Wieder, Thomas Seidel, Thierry Langer

Keywords: Contrastive Representation Learning, Neural Subgraph Matching, Virtual Screening, Pharmacophore Modeling

TL;DR: PharmacoMatch, a new contrastive learning approach, accelerates pharmacophore screening by encoding query-target relationships in the embedding space.

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 molecules. 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 investigations of the learned representations and evaluate PharmacoMatch as pre-screening tool in a zero-shot setting. We demonstrate significantly shorter runtimes and comparable performance metrics to existing solutions, providing a promising speed-up for screening very large datasets.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 3534