PharmacoMatch: Efficient 3D Pharmacophore Screening through Neural Subgraph Matching
<|reference_start|>PharmacoMatch: Efficient 3D Pharmacophore Screening through Neural Subgraph Matching: 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.<|reference_end|>
@article{rose2024pharmacomatch:,
    title={PharmacoMatch: Efficient 3D Pharmacophore Screening through Neural
  Subgraph Matching},
    author={Daniel Rose, Oliver Wieder, Thomas Seidel, Thierry Langer},
    journal={arXiv preprint arXiv:2409.06316},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06316},
    primaryClass={cs.LG cs.AI q-bio.QM}
}