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Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors
Author(s) -
Beker Wiktor,
Gajewska Ewa P.,
Badowski Tomasz,
Grzybowski Bartosz A.
Publication year - 2019
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201806920
Subject(s) - steric effects , diastereomer , diene , diels–alder reaction , chemistry , core (optical fiber) , computational chemistry , quantum chemical , stereochemistry , organic chemistry , computer science , molecule , catalysis , telecommunications , natural rubber
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.