PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching

TL;DR

PharmacoMatch introduces a neural subgraph matching approach for efficient 3D pharmacophore screening, significantly reducing runtime while maintaining comparable accuracy, thus enabling large-scale virtual screening in drug discovery.

Contribution

It presents a novel contrastive learning method that reinterprets pharmacophore screening as an approximate subgraph matching problem for faster large-scale screening.

Findings

Significantly shorter runtimes compared to existing methods.

Maintains comparable screening performance metrics.

Effective in zero-shot pre-screening scenarios.

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.