RLSynC:用于完成Synthon的离线-在线增强学习
Frazier N Baker1, Ziqi Chen1, Daniel Adu-Ampratwum2
1Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio 43210, United States.
Journal of chemical information and modeling
|August 18, 2024
概括
我们开发了RLSynC,这是一种用于化学合成计划的新型强化学习方法. 这种方法增强了反合成中的synthon完成,提高了高达14.9%的准确性.
科学领域:
- 计算化学的计算化学
- 化学中的人工智能.
- 有机合成规划 有机合成规划
背景情况:
- 复合成对于计划化学合成至关重要.
- 基于半模板的方法可以预测反应中心和完整的synthons.
- 现有的方法在高效的synthon完成方面面临挑战.
研究的目的:
- 引入RLSynC,一个离线在线强化学习方法用于synthon完成.
- 为了提高基于半模板的回复合成的准确性和效率.
- 为了使在合成规划中探索新的反应途径.
主要方法:
- RLSynC使用多个代理,每个synthon一个,用于同步,逐步完成.
- 该方法将线下培训数据与在线互动相结合,用于政策学习.
- 一个独立的前合成模型通过评估反应物的可能性来指导行动选择.
主要成果:
- RLSynC显著优于当前最先进的同步完成技术.
- 与现有方法相比,性能改善高达14.9%.
- 同步剂方法增强了对化学反应空间的探索.
结论:
- RLSynC为自动化合成规划提供了一种强大的新方法.
- 该方法证明了强化学习在推进逆合成方面的潜力.
- RLSynC为更高效和更全面的化学合成设计铺平了道路.
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