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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Samuel Boobier, David R. J. Hose, A. John Blacker, Bao N. Nguyen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/84cf9b720ddc4f8cb842ae7b8aa0604f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:84cf9b720ddc4f8cb842ae7b8aa0604f
record_format	dspace
spelling	oai:doaj.org-article:84cf9b720ddc4f8cb842ae7b8aa0604f2021-12-02T15:37:19ZMachine learning with physicochemical relationships: solubility prediction in organic solvents and water10.1038/s41467-020-19594-z2041-1723https://doaj.org/article/84cf9b720ddc4f8cb842ae7b8aa0604f2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19594-zhttps://doaj.org/toc/2041-1723Accurate 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.Samuel BoobierDavid R. J. HoseA. John BlackerBao N. NguyenNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Samuel Boobier David R. J. Hose A. John Blacker Bao N. Nguyen Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
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.
format	article
author	Samuel Boobier David R. J. Hose A. John Blacker Bao N. Nguyen
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 Portfolio
publishDate	2020
url	https://doaj.org/article/84cf9b720ddc4f8cb842ae7b8aa0604f
work_keys_str_mv	AT samuelboobier machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater AT davidrjhose machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater AT ajohnblacker machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater AT baonnguyen machinelearningwithphysicochemicalrelationshipssolubilitypredictioninorganicsolventsandwater
_version_	1718386243882975232

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

Ejemplares similares