Structure determination of an amorphous drug through large-scale NMR predictions

Determining the structure of amorphous solids is important for optimization of pharmaceutical formulations, but direct relation of molecular dynamics (MD) simulations and NMR to achieve this is challenging. Here, the authors use a machine learning model of chemical shifts to solve the atomic-level s...

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Autores principales:	Manuel Cordova, Martins Balodis, Albert Hofstetter, Federico Paruzzo, Sten O. Nilsson Lill, Emma S. E. Eriksson, Pierrick Berruyer, Bruno Simões de Almeida, Michael J. Quayle, Stefan T. Norberg, Anna Svensk Ankarberg, Staffan Schantz, Lyndon Emsley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/be50705d880e426e950afb5d4b6e95a0
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spelling	oai:doaj.org-article:be50705d880e426e950afb5d4b6e95a02021-12-02T16:51:29ZStructure determination of an amorphous drug through large-scale NMR predictions10.1038/s41467-021-23208-72041-1723https://doaj.org/article/be50705d880e426e950afb5d4b6e95a02021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23208-7https://doaj.org/toc/2041-1723Determining the structure of amorphous solids is important for optimization of pharmaceutical formulations, but direct relation of molecular dynamics (MD) simulations and NMR to achieve this is challenging. Here, the authors use a machine learning model of chemical shifts to solve the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR with predicted shifts for MD simulations of large systems.Manuel CordovaMartins BalodisAlbert HofstetterFederico ParuzzoSten O. Nilsson LillEmma S. E. ErikssonPierrick BerruyerBruno Simões de AlmeidaMichael J. QuayleStefan T. NorbergAnna Svensk AnkarbergStaffan SchantzLyndon EmsleyNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Manuel Cordova Martins Balodis Albert Hofstetter Federico Paruzzo Sten O. Nilsson Lill Emma S. E. Eriksson Pierrick Berruyer Bruno Simões de Almeida Michael J. Quayle Stefan T. Norberg Anna Svensk Ankarberg Staffan Schantz Lyndon Emsley Structure determination of an amorphous drug through large-scale NMR predictions
description	Determining the structure of amorphous solids is important for optimization of pharmaceutical formulations, but direct relation of molecular dynamics (MD) simulations and NMR to achieve this is challenging. Here, the authors use a machine learning model of chemical shifts to solve the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR with predicted shifts for MD simulations of large systems.
format	article
author	Manuel Cordova Martins Balodis Albert Hofstetter Federico Paruzzo Sten O. Nilsson Lill Emma S. E. Eriksson Pierrick Berruyer Bruno Simões de Almeida Michael J. Quayle Stefan T. Norberg Anna Svensk Ankarberg Staffan Schantz Lyndon Emsley
author_facet	Manuel Cordova Martins Balodis Albert Hofstetter Federico Paruzzo Sten O. Nilsson Lill Emma S. E. Eriksson Pierrick Berruyer Bruno Simões de Almeida Michael J. Quayle Stefan T. Norberg Anna Svensk Ankarberg Staffan Schantz Lyndon Emsley
author_sort	Manuel Cordova
title	Structure determination of an amorphous drug through large-scale NMR predictions
title_short	Structure determination of an amorphous drug through large-scale NMR predictions
title_full	Structure determination of an amorphous drug through large-scale NMR predictions
title_fullStr	Structure determination of an amorphous drug through large-scale NMR predictions
title_full_unstemmed	Structure determination of an amorphous drug through large-scale NMR predictions
title_sort	structure determination of an amorphous drug through large-scale nmr predictions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/be50705d880e426e950afb5d4b6e95a0
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Structure determination of an amorphous drug through large-scale NMR predictions

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