Exploring structure-property relationships in magnesium dissolution modulators

Abstract Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-ba...

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Autores principales:	Tim Würger, Di Mei, Bahram Vaghefinazari, David A. Winkler, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Robert H. Meißner, Christian Feiler
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/9927113aea7c4119b7eabc65571a210f
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spelling	oai:doaj.org-article:9927113aea7c4119b7eabc65571a210f2021-12-02T11:45:58ZExploring structure-property relationships in magnesium dissolution modulators10.1038/s41529-020-00148-z2397-2106https://doaj.org/article/9927113aea7c4119b7eabc65571a210f2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41529-020-00148-zhttps://doaj.org/toc/2397-2106Abstract Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.Tim WürgerDi MeiBahram VaghefinazariDavid A. WinklerSviatlana V. LamakaMikhail L. ZheludkevichRobert H. MeißnerChristian FeilerNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENnpj Materials Degradation, Vol 5, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Tim Würger Di Mei Bahram Vaghefinazari David A. Winkler Sviatlana V. Lamaka Mikhail L. Zheludkevich Robert H. Meißner Christian Feiler Exploring structure-property relationships in magnesium dissolution modulators
description	Abstract Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.
format	article
author	Tim Würger Di Mei Bahram Vaghefinazari David A. Winkler Sviatlana V. Lamaka Mikhail L. Zheludkevich Robert H. Meißner Christian Feiler
author_facet	Tim Würger Di Mei Bahram Vaghefinazari David A. Winkler Sviatlana V. Lamaka Mikhail L. Zheludkevich Robert H. Meißner Christian Feiler
author_sort	Tim Würger
title	Exploring structure-property relationships in magnesium dissolution modulators
title_short	Exploring structure-property relationships in magnesium dissolution modulators
title_full	Exploring structure-property relationships in magnesium dissolution modulators
title_fullStr	Exploring structure-property relationships in magnesium dissolution modulators
title_full_unstemmed	Exploring structure-property relationships in magnesium dissolution modulators
title_sort	exploring structure-property relationships in magnesium dissolution modulators
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9927113aea7c4119b7eabc65571a210f
work_keys_str_mv	AT timwurger exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT dimei exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT bahramvaghefinazari exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT davidawinkler exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT sviatlanavlamaka exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT mikhaillzheludkevich exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT roberthmeißner exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators AT christianfeiler exploringstructurepropertyrelationshipsinmagnesiumdissolutionmodulators
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Exploring structure-property relationships in magnesium dissolution modulators

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