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Artificial Intelligence: The Future for Organic Chemistry?

[Image: see text] On the basis of a recent article “Predicting reaction performance in C–N cross-coupling using machine learning” that appeared in Science, we had decided to highlight the way forward for artificial intelligence in chemistry. Synthesis of molecules remains one of the most important c...

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Autores principales: Peiretti, Franck, Brunel, Jean Michel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6645362/
https://www.ncbi.nlm.nih.gov/pubmed/31458044
http://dx.doi.org/10.1021/acsomega.8b01773
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author Peiretti, Franck
Brunel, Jean Michel
author_facet Peiretti, Franck
Brunel, Jean Michel
author_sort Peiretti, Franck
collection PubMed
description [Image: see text] On the basis of a recent article “Predicting reaction performance in C–N cross-coupling using machine learning” that appeared in Science, we had decided to highlight the way forward for artificial intelligence in chemistry. Synthesis of molecules remains one of the most important challenges in organic chemistry, and the standard approach involved by a chemist to solve a problem is based on experience and constitutes a repetitive, time-consuming task, often resulting in nonoptimized solutions. Thus, considering the recent phenomenal progresses that have been made in machine learning, there is little doubt that these systems, once fully operational in organic chemistry, will dramatically speed up development of new drugs and will constitute the future of chemistry.
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spelling pubmed-66453622019-08-27 Artificial Intelligence: The Future for Organic Chemistry? Peiretti, Franck Brunel, Jean Michel ACS Omega [Image: see text] On the basis of a recent article “Predicting reaction performance in C–N cross-coupling using machine learning” that appeared in Science, we had decided to highlight the way forward for artificial intelligence in chemistry. Synthesis of molecules remains one of the most important challenges in organic chemistry, and the standard approach involved by a chemist to solve a problem is based on experience and constitutes a repetitive, time-consuming task, often resulting in nonoptimized solutions. Thus, considering the recent phenomenal progresses that have been made in machine learning, there is little doubt that these systems, once fully operational in organic chemistry, will dramatically speed up development of new drugs and will constitute the future of chemistry. American Chemical Society 2018-10-16 /pmc/articles/PMC6645362/ /pubmed/31458044 http://dx.doi.org/10.1021/acsomega.8b01773 Text en Copyright © 2018 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Peiretti, Franck
Brunel, Jean Michel
Artificial Intelligence: The Future for Organic Chemistry?
title Artificial Intelligence: The Future for Organic Chemistry?
title_full Artificial Intelligence: The Future for Organic Chemistry?
title_fullStr Artificial Intelligence: The Future for Organic Chemistry?
title_full_unstemmed Artificial Intelligence: The Future for Organic Chemistry?
title_short Artificial Intelligence: The Future for Organic Chemistry?
title_sort artificial intelligence: the future for organic chemistry?
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6645362/
https://www.ncbi.nlm.nih.gov/pubmed/31458044
http://dx.doi.org/10.1021/acsomega.8b01773
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