ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916331823038464 |
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| author | Bou, Albert Thomas, Morgan Dittert, Sebastian Ramírez, Carles Navarro Majewski, Maciej Wang, Ye Patel, Shivam Tresadern, Gary Ahmad, Mazen Moens, Vincent Sherman, Woody Sciabola, Simone De Fabritiis, Gianni |
| author_facet | Bou, Albert Thomas, Morgan Dittert, Sebastian Ramírez, Carles Navarro Majewski, Maciej Wang, Ye Patel, Shivam Tresadern, Gary Ahmad, Mazen Moens, Vincent Sherman, Woody Sciabola, Simone De Fabritiis, Gianni |
| contents | In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at \url{https://github.com/acellera/acegen-open} and available for use under the MIT license. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04657 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ACEGEN: Reinforcement learning of generative chemical agents for drug discovery Bou, Albert Thomas, Morgan Dittert, Sebastian Ramírez, Carles Navarro Majewski, Maciej Wang, Ye Patel, Shivam Tresadern, Gary Ahmad, Mazen Moens, Vincent Sherman, Woody Sciabola, Simone De Fabritiis, Gianni Machine Learning Artificial Intelligence Biomolecules In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at \url{https://github.com/acellera/acegen-open} and available for use under the MIT license. |
| title | ACEGEN: Reinforcement learning of generative chemical agents for drug discovery |
| topic | Machine Learning Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2405.04657 |