AI Article Synopsis
- A new learning model combines machine learning and reaction network techniques to predict products and reaction pathways in organic chemistry.
- It was trained on 50 fundamental organic reactions and achieved a 68.6% top-5 accuracy when predicting outcomes for 35 test reactions.
- The model also identified key intermediate structures and established basic reaction rules, such as the Markovnikov rule, to enhance understanding of organic reactions.
Article 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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| http://dx.doi.org/10.1039/d3cc03890d | DOI Listing |
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