Prediction of major regio-, site-, and diastereoisomers in Diels-Alder reactions using machine-learning: The importance of physically meaningful descriptors

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Title
Prediction of major regio-, site-, and diastereoisomers in Diels-Alder reactions using machine-learning: The importance of physically meaningful descriptors
Author(s)
Wiktor Beker; Ewa Gajewska; Tomasz Badowski; Bartosz A. Grzybowski
Publication Date
2019-03
Journal
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, v.58, no.14, pp.4515 - 4519
Publisher
WILEY-V C H VERLAG GMBH
Abstract
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. (c) 2013 Wiley-VCH verlag Gmbh & Co. KGaA, Weinheim
URI
https://pr.ibs.re.kr/handle/8788114/5680
ISSN
1433-7851
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > Journal Papers (저널논문)
Files in This Item:
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