Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Name
d2sc05089g.pdf
Description
Published version
Size
1.27 MB
Format
Adobe PDF
Checksum (MD5)
581b5ded4505abfee5c1a7a3a8096645
Author(s)
Tu, Zhengkai
Stuyver, Thijs
Coley, Connor W
Date Issued
January 4, 2023
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d2sc05089g
