增强蒙特卡洛树寻找回复合成的方法
Ton M Blackshaw1,2, Joseph C Davies1, Kristian T Spoerer2
1School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, U.K.
Journal of chemical information and modeling
|June 13, 2025
概括
蒙特卡罗树搜索 (MCTS) 的两个新增增强,eUCT和dUCT,显著减少了计算机辅助合成的计算时间. 这些人工智能工具也增加了对复杂分子发现的化学路径的数量.
科学领域:
- 有机化学 有机化学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 计算机辅助合成程序通常使用神经网络和启发式搜索算法.
- 蒙特卡洛树搜索 (MCTS) 算法是许多这样的工具的关键组成部分.
- 优化计算效率和路线发现对于实际应用至关重要.
研究的目的:
- 引入两个新的增强,eUCT和dUCT,用于计算机辅助合成的MCTS算法.
- 评估这些改进对计算时间和发现的合成路径数量的影响.
- 将这些改进整合到现有的开源工具中,并评估它们在大型分子数据集上的性能.
主要方法:
- 开发和实施两个MCTS增强:eUCT和dUCT.
- 将eUCT和dUCT集成到AiZynthFinder程序和AI4Green电子实验室笔记本中.
- 使用存储效率高的库存文件来最大限度地减少计算碳足迹.
- 在来自ChEMBL的1500个重分子和50,000个分子集上测试增强的算法.
主要成果:
- 对于解决1500个重分子,eUCT和dUCT都将计算时钟时间减少了多达50%.
- 对于两个测试数据集,dUCT增强增加了每个分子发现的合成路径的数量.
- 在5万个分子的集合中,eUCT和dUCT-v2比未增强的MCTS解决了600-900多个分子.
- 在150秒的时间限制下,dUCT-v1发现与未增强的MCTS相比,为50,000个目标增加了大约500万条路线.
结论:
- 拟议的eUCT和dUCT改进显著提高了计算机辅助合成中MCTS的效率和路线查找能力.
- 这些进展有助于更快,更全面地探索有机化学家的合成可能性.
- 集成到像AI4Green这样的开源平台中,促进了更广泛的采用和可持续的计算化学实践.
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