Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

Deep neural networks are potent tools for computational chemistry, but experimental feed data can limit their reach. Here the authors develop deep neural network data augmentation models to predict octanol–water partition coefficients (log P) of a variety of tautomers.

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Autores principales:	Nadin Ulrich, Kai-Uwe Goss, Andrea Ebert
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/9d61fbcad06746a6a19e36cf2342853f
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spelling	oai:doaj.org-article:9d61fbcad06746a6a19e36cf2342853f2021-12-02T17:39:43ZExploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation10.1038/s42004-021-00528-92399-3669https://doaj.org/article/9d61fbcad06746a6a19e36cf2342853f2021-06-01T00:00:00Zhttps://doi.org/10.1038/s42004-021-00528-9https://doaj.org/toc/2399-3669Deep neural networks are potent tools for computational chemistry, but experimental feed data can limit their reach. Here the authors develop deep neural network data augmentation models to predict octanol–water partition coefficients (log P) of a variety of tautomers.Nadin UlrichKai-Uwe GossAndrea EbertNature PortfolioarticleChemistryQD1-999ENCommunications Chemistry, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemistry QD1-999
spellingShingle	Chemistry QD1-999 Nadin Ulrich Kai-Uwe Goss Andrea Ebert Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
description	Deep neural networks are potent tools for computational chemistry, but experimental feed data can limit their reach. Here the authors develop deep neural network data augmentation models to predict octanol–water partition coefficients (log P) of a variety of tautomers.
format	article
author	Nadin Ulrich Kai-Uwe Goss Andrea Ebert
author_facet	Nadin Ulrich Kai-Uwe Goss Andrea Ebert
author_sort	Nadin Ulrich
title	Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
title_short	Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
title_full	Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
title_fullStr	Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
title_full_unstemmed	Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
title_sort	exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9d61fbcad06746a6a19e36cf2342853f
work_keys_str_mv	AT nadinulrich exploringtheoctanolwaterpartitioncoefficientdatasetusingdeeplearningtechniquesanddataaugmentation AT kaiuwegoss exploringtheoctanolwaterpartitioncoefficientdatasetusingdeeplearningtechniquesanddataaugmentation AT andreaebert exploringtheoctanolwaterpartitioncoefficientdatasetusingdeeplearningtechniquesanddataaugmentation
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Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

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