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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2018
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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. |
format | Online Article Text |
id | pubmed-6645362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
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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