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Quantum Machine Learning

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

PubMed

Authors: Beker W, Gajewska EP, Badowski T, Grzybowski BA

Year

2019

Paper ID

1637

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

Citations

146

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.

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Machine learning can predict the major regio-, site-, and diastereoselective outcomes of Diels-Alder reactions better than standard quantum-mechanical methods and with...

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References & Citation Signals

[1] DOI https://doi.org/10.1002/anie.201806920 [2] Publisher https://pubmed.ncbi.nlm.nih.gov/30398688/

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