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以经验为指导的蒙特卡罗树搜索为回归合成规划
Siqi Hong1, Hankz Hankui Zhuo2, Kebing Jin1
1School of Computer Science and Engineering, Sun Yat-Sen University, East Outer Ring Road, 510006, Guangzhou, Guangdong, China.
Communications chemistry
|June 10, 2023
概括
本研究介绍了一种经验导向的蒙特卡罗树搜索 (EG-MCTS) 方法,以改进回合成规划. 通过从化学经验中学习,EG-MCTS有效地识别最佳合成路径,优于现有方法.
科学领域:
- 计算化学是一种计算化学.
- 有机合成 有机合成
- 化学领域的人工智能
背景情况:
- 复合成规划面临着组合性的可能性爆炸,挑战化学家在选择最佳合成路径.
- 目前指导逆合成的方法依赖于有限的化学知识或昂贵的估计技术.
研究的目的:
- 开发一种新的,经验导向的蒙特卡罗树搜索 (EG-MCTS) 算法,用于高效和有效的回合成规划.
- 解决复杂分子合成中现有的评分函数和估计方法的局限性.
主要方法:
- 在蒙特卡罗树搜索框架内实施经验指导网络,以从合成数据中学习.
- 在搜索过程中使用合成体验,而不是传统的推广方法.
主要成果:
- 与USPTO数据集的最先进方法相比,EG-MCTS在效率和有效性方面取得了显著的改进.
- 计算机生成的合成路径与比较文献实验中报告的路径密切匹配.
- 在设计真实药物化合物的路径时,成功地应用EG-MCTS,帮助化学家进行逆合成分析.
结论:
- EG-MCTS提供了一种强大的,经验驱动的方法来克服复合规划中的挑战.
- 开发的方法增强了发现复杂分子最佳合成途径的能力.
- EG-MCTS显示出有很大的潜力,可以帮助化学家在实践中发现和开发药物.
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