Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Accurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computatio...

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Main Authors:	Samuel Boobier, David R. J. Hose, A. John Blacker, Bao N. Nguyen
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-11-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-19594-z

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spelling	doaj-84cf9b720ddc4f8cb842ae7b8aa0604f2021-05-11T08:17:00ZengNature Publishing GroupNature Communications2041-17232020-11-0111111010.1038/s41467-020-19594-zMachine learning with physicochemical relationships: solubility prediction in organic solvents and waterSamuel Boobier0David R. J. Hose1A. John Blacker2Bao N. Nguyen3Institute of Process Research & Development, School of Chemistry, University of Leeds, Woodhouse LaneChemical Development, Pharmaceutical Technology and Development, Operations, AstraZenecaInstitute of Process Research & Development, School of Chemistry, University of Leeds, Woodhouse LaneInstitute of Process Research & Development, School of Chemistry, University of Leeds, Woodhouse LaneAccurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computational chemistry.https://doi.org/10.1038/s41467-020-19594-z
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Samuel Boobier David R. J. Hose A. John Blacker Bao N. Nguyen
spellingShingle	Samuel Boobier David R. J. Hose A. John Blacker Bao N. Nguyen Machine learning with physicochemical relationships: solubility prediction in organic solvents and water Nature Communications
author_facet	Samuel Boobier David R. J. Hose A. John Blacker Bao N. Nguyen
author_sort	Samuel Boobier
title	Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
title_short	Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
title_full	Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
title_fullStr	Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
title_full_unstemmed	Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
title_sort	machine learning with physicochemical relationships: solubility prediction in organic solvents and water
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2020-11-01
description	Accurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computational chemistry.
url	https://doi.org/10.1038/s41467-020-19594-z
work_keys_str_mv	AT samuelboobier machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater AT davidrjhose machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater AT ajohnblacker machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater AT baonnguyen machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater
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Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

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