计算机辅助合成规划 (CASP) 和机器学习:优化化学反应条件
Yu Han1, Mingjing Deng1, Ke Liu1
1State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China.
Chemistry (Weinheim an der Bergstrasse, Germany)
|July 31, 2024
概括
计算机辅助合成计划 (CASP) 使用机器学习来优化化学反应. 本综述涵盖了数据来源,描述器选择,以及在反应条件优化中提高预测准确性的模型.
科学领域:
- 化学 化学 化学
- 机器学习 机器学习
- 计算化学计算化学
背景情况:
- 由于机器学习的进步,计算机辅助合成规划 (CASP) 越来越重要.
- 目前的CASP专注于逆合成和前预测,但优化反应条件具有挑战性.
- 有效的优化需要仔细选择复杂数据集的描述符和模型.
研究的目的:
- 审查使用CASP优化化学反应条件的最新研究趋势.
- 引导研究人员选择适当的描述符,模型和评估指标.
- 在条件优化任务中提高预测准确度.
主要方法:
- 对CASP用于反应条件优化的文献综述.
- 对数据来源和描述符选择标准的分析.
- 评估各种响应模型和结果指标.
主要成果:
- 在条件优化中确定了影响预测准确性的关键因素.
- 基于数据集特征的描述符选择的细分标准.
- 总结了响应建模和结果评估中的当前方法.
结论:
- 优化反应条件是CASP的一个关键但未被充分探索的领域.
- 对描述符和模型的知情选择对于高预测准确性至关重要.
- 本综述为推进化学合成条件优化研究提供了一个框架.
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