Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors

Wiktor Beker; Ewa P Gajewska; Tomasz Badowski; Bartosz A Grzybowski

doi:10.1002/anie.201806920

Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors

Angew Chem Int Ed Engl. 2019 Mar 26;58(14):4515-4519. doi: 10.1002/anie.201806920. Epub 2018 Dec 4.

Authors

Wiktor Beker¹, Ewa P Gajewska¹, Tomasz Badowski¹, Bartosz A Grzybowski^{1

2}

Affiliations

¹ Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224, Warsaw, Poland.
² Center for Soft and Living Matter and Department of Chemistry, UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, South Korea.

PMID: 30398688
DOI: 10.1002/anie.201806920

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.

Keywords: Diels-Alder reaction; Random Forest; machine learning; neural networks; selectivity.

Abstract

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