ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning
Abstract Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and...
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| Główni autorzy: | , , , , , , , , , , , , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
Nature Portfolio
2024-11-01
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| Seria: | Nature Communications |
| Dostęp online: | https://doi.org/10.1038/s41467-024-54456-y |
| Etykiety: |
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