这就是为什么S S S S S S
Yanfei Guan1, Taegyo Lee1, Ke Wang2
1Chemical Research and Development, Groton Laboratories, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States.
Journal of chemical information and modeling
|June 5, 2023
概括
本研究介绍了一种混合机器学习 (ML) 和过渡状态理论 (TST) 方法,用于预测化学反应的区域选择性. 该方法通过使用DFT计算来加速预测,只有当ML的信心较低时.
科学领域:
- 计算化学是一种计算化学.
- 预测化学反应的化学反应
- 机器学习在化学中的应用
背景情况:
- 预测区域选择性对于高效的药物合成至关重要.
- 像DFT/TST和ML这样的当前方法有局限性 (耗时或依赖数据).
研究的目的:
- 开发一种更快,更准确的方法来预测反应区域选择性.
- 整合ML和DFT/TST以优化计算资源.
主要方法:
- 一个混合工作流,结合了ML和DFT计算.
- 对于低置信度的ML预测,DFT计算被选择性触发.
- 该模型在SNR反应数据集 (Pfizer和USPTO) 上进行了训练和测试.
主要成果:
- 在Pfizer数据集上达到96.3%的准确性,在USPTO数据集上达到94.7%.
- 与传统的TST方法相比,显著减少了计算时间.
结论:
- 混合ML-DFT方法为区域选择性预测提供了快速而准确的解决方案.
- 这种方法可以通过尽量减少非生产性的合成努力来简化药物发现.
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