Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions
Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions
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DOI：
10.1021/acs.orglett.0c03083
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发表时间：
2020-11-06
期刊：
影响因子：
5.2
通讯作者：
Chopra, Gaurav
中科院分区：
文献类型：
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作者：
Jethava, Krupal P.;Fine, Jonathan;Chopra, Gaurav
我们介绍了化学反应性流程图，以帮助化学家解释反应结果，使用统计学上强大的机器学习模型训练少量的反应。我们开发了快速N-磺酰亚胺多组分反应，用于了解反应性并生成训练数据。用密度泛函理论研究了加速反应机理。通过模型学习的直观化学特征准确地预测了N-磺酰亚胺与不同羧酸的非均相反应性。验证的预测表明，反应结果的解释是有用的人类化学家。
We introduce chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions. We developed fast N-sulfonylimine multicomponent reactions for understanding reactivity and to generate training data. Accelerated reactivity mechanisms were investigated using density functional theory. Intuitive chemical features learned by the model accurately predicted heterogeneous reactivity of N-sulfonylimine with different carboxylic acids. Validation of the predictions shows that reaction outcome interpretation is useful for human chemists.