تقرير
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
| العنوان: | ACEGEN: Reinforcement learning of generative chemical agents for drug discovery |
|---|---|
| المؤلفون: | 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 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Biomolecules |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.04657 |
| رقم الأكسشن: | edsarx.2405.04657 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |