| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Nature Communications, Vol 12, Iss 1, Pp 1-8 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-021-23208-7 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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