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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Author(s)
Tu, Zhengkai; Stuyver, Thijs; Coley, Connor W
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Abstract
The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.
Date issued
2023-01-04
URI
https://hdl.handle.net/1721.1/148455
Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Journal
Chemical Science
Publisher
Royal Society of Chemistry (RSC)
Citation
Tu, Zhengkai, Stuyver, Thijs and Coley, Connor W. 2023. "Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery." Chemical Science, 14 (2).
Version: Final published version

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