RapidDock: Unlocking Proteome-scale Molecular Docking
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916464214147072 |
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| author | Powalski, Rafał Klockiewicz, Bazyli Jaśkowski, Maciej Topolski, Bartosz Dąbrowski-Tumański, Paweł Wiśniewski, Maciej Kuciński, Łukasz Miłoś, Piotr Plewczynski, Dariusz |
| author_facet | Powalski, Rafał Klockiewicz, Bazyli Jaśkowski, Maciej Topolski, Bartosz Dąbrowski-Tumański, Paweł Wiśniewski, Maciej Kuciński, Łukasz Miłoś, Piotr Plewczynski, Dariusz |
| contents | Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential drugs against all relevant proteins, which often results in missed drug candidates or unexpected side effects occurring in clinical trials. To address this gap, we introduce RapidDock, an efficient transformer-based model for blind molecular docking. RapidDock achieves at least a $100 \times$ speed advantage over existing methods without compromising accuracy. On the Posebusters and DockGen benchmarks, our method achieves $52.1\%$ and $44.0\%$ success rates ($\text{RMSD}<2$Å), respectively. The average inference time is $0.04$ seconds on a single GPU, highlighting RapidDock's potential for large-scale docking studies. We examine the key features of RapidDock that enable leveraging the transformer architecture for molecular docking, including the use of relative distance embeddings of $3$D structures in attention matrices, pre-training on protein folding, and a custom loss function invariant to molecular symmetries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RapidDock: Unlocking Proteome-scale Molecular Docking Powalski, Rafał Klockiewicz, Bazyli Jaśkowski, Maciej Topolski, Bartosz Dąbrowski-Tumański, Paweł Wiśniewski, Maciej Kuciński, Łukasz Miłoś, Piotr Plewczynski, Dariusz Biomolecules Machine Learning Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential drugs against all relevant proteins, which often results in missed drug candidates or unexpected side effects occurring in clinical trials. To address this gap, we introduce RapidDock, an efficient transformer-based model for blind molecular docking. RapidDock achieves at least a $100 \times$ speed advantage over existing methods without compromising accuracy. On the Posebusters and DockGen benchmarks, our method achieves $52.1\%$ and $44.0\%$ success rates ($\text{RMSD}<2$Å), respectively. The average inference time is $0.04$ seconds on a single GPU, highlighting RapidDock's potential for large-scale docking studies. We examine the key features of RapidDock that enable leveraging the transformer architecture for molecular docking, including the use of relative distance embeddings of $3$D structures in attention matrices, pre-training on protein folding, and a custom loss function invariant to molecular symmetries. |
| title | RapidDock: Unlocking Proteome-scale Molecular Docking |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2411.00004 |