Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?

Machines learn chemistry: An artificial intelligence algorithm has learned to predict the outcomes of C−N coupling reactions from a few thousand nanomole‐scale experiments. This Highlight discusses this work in the context of other state‐of‐the‐art approaches for predicting the yields of organic rea...

Descripción completa

Detalles Bibliográficos
Autores principales: Maryasin, Boris, Marquetand, Philipp, Maulide, Nuno
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Highlights
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033144/
https://www.ncbi.nlm.nih.gov/pubmed/29701305
http://dx.doi.org/10.1002/anie.201803562
_version_ 1783337645741441024
author Maryasin, Boris
Marquetand, Philipp
Maulide, Nuno
author_facet Maryasin, Boris
Marquetand, Philipp
Maulide, Nuno
author_sort Maryasin, Boris
collection PubMed
description Machines learn chemistry: An artificial intelligence algorithm has learned to predict the outcomes of C−N coupling reactions from a few thousand nanomole‐scale experiments. This Highlight discusses this work in the context of other state‐of‐the‐art approaches for predicting the yields of organic reactions and explains the significance of the results.[Image: see text]
format Online
Article
Text
id pubmed-6033144
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-60331442018-07-12 Machine Learning for Organic Synthesis: Are Robots Replacing Chemists? Maryasin, Boris Marquetand, Philipp Maulide, Nuno Angew Chem Int Ed Engl Highlights Machines learn chemistry: An artificial intelligence algorithm has learned to predict the outcomes of C−N coupling reactions from a few thousand nanomole‐scale experiments. This Highlight discusses this work in the context of other state‐of‐the‐art approaches for predicting the yields of organic reactions and explains the significance of the results.[Image: see text] John Wiley and Sons Inc. 2018-04-27 2018-06-11 /pmc/articles/PMC6033144/ /pubmed/29701305 http://dx.doi.org/10.1002/anie.201803562 Text en © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Highlights
Maryasin, Boris
Marquetand, Philipp
Maulide, Nuno
Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
title Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
title_full Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
title_fullStr Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
title_full_unstemmed Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
title_short Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
title_sort machine learning for organic synthesis: are robots replacing chemists?
topic Highlights
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033144/
https://www.ncbi.nlm.nih.gov/pubmed/29701305
http://dx.doi.org/10.1002/anie.201803562
work_keys_str_mv AT maryasinboris machinelearningfororganicsynthesisarerobotsreplacingchemists
AT marquetandphilipp machinelearningfororganicsynthesisarerobotsreplacingchemists
AT maulidenuno machinelearningfororganicsynthesisarerobotsreplacingchemists

Ejemplares similares