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 Aug. 2023
Accepted
12 Sept. 2023
First published
29 Sept. 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

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements