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Updated: Jul 15, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches
Tomonori Ida1, Honoka Kojima1, Yuta Hori2
1Division of Material Chemistry, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 920-1192, Japan. ida@se.kanazawa-u.ac.jp.
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.
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