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

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. By training 50 fundamental organic reactions, the learning model predicted the products and pathways of 35 test reactions. The model identified the key fragment structures of the reaction intermediates.

Automated summary

A learning model that combines machine learning and reaction network approaches is proposed to predict both products and reaction pathways. The model shows high accuracy in predicting the products and pathways of test reactions and can identify key fragment structures of reaction intermediates.