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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
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 pred...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811563/ https://www.ncbi.nlm.nih.gov/pubmed/36743887 http://dx.doi.org/10.1039/d2sc05089g |
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author | Tu, Zhengkai Stuyver, Thijs Coley, Connor W. |
author_facet | Tu, Zhengkai Stuyver, Thijs Coley, Connor W. |
author_sort | Tu, Zhengkai |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9811563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-98115632023-02-03 Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery Tu, Zhengkai Stuyver, Thijs Coley, Connor W. Chem Sci Chemistry 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. The Royal Society of Chemistry 2022-11-28 /pmc/articles/PMC9811563/ /pubmed/36743887 http://dx.doi.org/10.1039/d2sc05089g Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Tu, Zhengkai Stuyver, Thijs Coley, Connor W. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
title | Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
title_full | Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
title_fullStr | Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
title_full_unstemmed | Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
title_short | Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
title_sort | predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811563/ https://www.ncbi.nlm.nih.gov/pubmed/36743887 http://dx.doi.org/10.1039/d2sc05089g |
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