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.

• Publication year

  2023

• Venue

  Chemical Communications

• Publication date

  2023-09-29

• Fields of study

  Medicine, Chemistry, Computer Science

• Identifiers
  DOI 10.1039/d3cc03890dPMID 37773321
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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