期刊
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
卷 58, 期 14, 页码 4515-4519出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.201806920
关键词
Diels-Alder reaction; machine learning; neural networks; Random Forest; selectivity
资金
- U.S. DARPA (Make-It Award) [69461-CH-DRP, W911NF1610384]
- Institute for Basic Science Korea [IBS-R020-D1]
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.
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