PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912273998544896 |
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| author | Rose, Daniel Wieder, Oliver Seidel, Thomas Langer, Thierry |
| author_facet | Rose, Daniel Wieder, Oliver Seidel, Thomas Langer, Thierry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06316 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching Rose, Daniel Wieder, Oliver Seidel, Thomas Langer, Thierry Machine Learning Artificial Intelligence Quantitative Methods 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. |
| title | PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2409.06316 |