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![Solid-phase Synthesis of [4.4] Spirocyclic Oximes](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F58508.jpg&w=3840&q=50)
Solid-phase Synthesis of [4.4] Spirocyclic Oximes
Published on: February 6, 2019
在回复合成问题上比较搜索算法
Milo Roucairol1, Tristan Cazenave1
1LAMSADE, Université Paris Dauphine - PSL, Paris, France.
新的算法,嵌套的蒙特卡罗搜索和贪的最佳首次搜索,在使用AiZynthFinder的回归合成预测中超过了标准的蒙特卡罗树搜索. 进一步的改进取决于提高政策网络的质量.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
背景情况:
- 反合成对于合成路线规划至关重要.
- AiZynthFinder是一个开源工具,用于回合成分析.
- 在AiZynthFinder中评估和改进搜索算法对于其有效性至关重要.
研究的目的:
- 为了比较嵌套蒙特卡罗搜索 (NMCS) 和贪最佳首次搜索 (GBFS) 与AiZynthFinder中的基础蒙特卡罗树搜索 (MCTS) 的性能.
- 评估不同搜索算法对回合成预测准确性和效率的影响.
- 识别限制和改善AiZynthFinder搜索能力的未来方向.
主要方法:
- 在AiZynthFinder框架内实施和测试NMCS和GBFS算法.
- 使用由拜耳化学家从PubChem数据库中策划的基准数据集.
- 对NMCS,GBFS和基础MCTS性能指标进行比较分析.
主要成果:
- 在回合成预测中,NMCS和GBFS都表现出与基MCTS相比的优异性能.
- NMCS在GBFS上显示出轻微的优势,特别是在实验季后赛启发式时.
- 发现所有测试的搜索算法的性能都受到底层政策网络的质量限制.
结论:
- 与使用AiZynthFinder进行回合成的标准MCTS相比,NMCS和GBFS具有显著的改进.
- 加强政策网络被认为是进一步提高预测准确性的关键下一步.
- 这项研究为优化人工智能驱动的回合成规划工具提供了有价值的见解.
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