Determining molecular properties with differential mobility spectrometry and machine learning

The fast and accurate determination of molecular properties is particularly crucial in drug discovery. Here, the authors employ supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten classes of drug candidates and predict several condensed-phase prop...

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Autores principales: Stephen W. C. Walker, Ahdia Anwar, Jarrod M. Psutka, Jeff Crouse, Chang Liu, J. C. Yves Le Blanc, Justin Montgomery, Gilles H. Goetz, John S. Janiszewski, J. Larry Campbell, W. Scott Hopkins
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Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/e5545143641641f8afbe1987126bcf2f
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spelling oai:doaj.org-article:e5545143641641f8afbe1987126bcf2f2021-12-02T16:49:18ZDetermining molecular properties with differential mobility spectrometry and machine learning10.1038/s41467-018-07616-w2041-1723https://doaj.org/article/e5545143641641f8afbe1987126bcf2f2018-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07616-whttps://doaj.org/toc/2041-1723The fast and accurate determination of molecular properties is particularly crucial in drug discovery. Here, the authors employ supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten classes of drug candidates and predict several condensed-phase properties.Stephen W. C. WalkerAhdia AnwarJarrod M. PsutkaJeff CrouseChang LiuJ. C. Yves Le BlancJustin MontgomeryGilles H. GoetzJohn S. JaniszewskiJ. Larry CampbellW. Scott HopkinsNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-7 (2018)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Stephen W. C. Walker
Ahdia Anwar
Jarrod M. Psutka
Jeff Crouse
Chang Liu
J. C. Yves Le Blanc
Justin Montgomery
Gilles H. Goetz
John S. Janiszewski
J. Larry Campbell
W. Scott Hopkins
Determining molecular properties with differential mobility spectrometry and machine learning
description The fast and accurate determination of molecular properties is particularly crucial in drug discovery. Here, the authors employ supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten classes of drug candidates and predict several condensed-phase properties.
format article
author Stephen W. C. Walker
Ahdia Anwar
Jarrod M. Psutka
Jeff Crouse
Chang Liu
J. C. Yves Le Blanc
Justin Montgomery
Gilles H. Goetz
John S. Janiszewski
J. Larry Campbell
W. Scott Hopkins
author_facet Stephen W. C. Walker
Ahdia Anwar
Jarrod M. Psutka
Jeff Crouse
Chang Liu
J. C. Yves Le Blanc
Justin Montgomery
Gilles H. Goetz
John S. Janiszewski
J. Larry Campbell
W. Scott Hopkins
author_sort Stephen W. C. Walker
title Determining molecular properties with differential mobility spectrometry and machine learning
title_short Determining molecular properties with differential mobility spectrometry and machine learning
title_full Determining molecular properties with differential mobility spectrometry and machine learning
title_fullStr Determining molecular properties with differential mobility spectrometry and machine learning
title_full_unstemmed Determining molecular properties with differential mobility spectrometry and machine learning
title_sort determining molecular properties with differential mobility spectrometry and machine learning
publisher Nature Portfolio
publishDate 2018
url https://doaj.org/article/e5545143641641f8afbe1987126bcf2f
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