Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors
Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors
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DOI：
10.1002/anie.201806920
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发表时间：
2019-03-26
影响因子：
16.6
通讯作者：
Grzybowski, Bartosz A.
中科院分区：
文献类型：
--
作者：
Beker, Wiktor;Gajewska, Ewa P.;Grzybowski, Bartosz A.
机器学习可以预测主要的区域，地点，和非对映选择性的Diels-Alder反应的结果比标准的量子力学方法更好，并具有超过90%的准确度，条件是i）二烯/亲二烯底物由反映其取代基的电子和空间特征的物理有机描述符表示，和ii）编码这些取代基相对于反应核心的位置（矢量化）以信息化的方式。
Machine learning can predict the major regio-, site-, and diastereoselective outcomes of Diels-Alder reactions better than standard quantum-mechanical methods and with accuracies exceeding 90% provided that i) the diene/dienophile substrates are represented by physical-organic descriptors reflecting the electronic and steric characteristics of their substituents and ii) the positions of such substituents relative to the reaction core are encoded (vectorized) in an informative way.