Issue 83, 2023

Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches

Abstract

A learning model is proposed that predicts both products and reaction pathways by combining machine learning and reaction network approaches. By training 50 fundamental organic reactions, the learning model predicted the products and pathways of 35 test reactions with a top-5 accuracy of 68.6%. The model identified the key fragment structures of the intermediates and could be classified as several basic reaction rules in the context of organic chemistry, such as the Markovnikov rule.

Graphical abstract: Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches

Supplementary files

Article information

Article type
Communication
Submitted
11 Сер 2023
Accepted
12 Вер 2023
First published
29 Вер 2023
This article is Open Access
Creative Commons BY-NC license

Chem. Commun., 2023,59, 12439-12442

Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches

T. Ida, H. Kojima and Y. Hori, Chem. Commun., 2023, 59, 12439 DOI: 10.1039/D3CC03890D

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