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research article

De Novo Crystal Structure Determination from Machine Learned Chemical Shifts

Balodis, Martins  
•
Cordova, Manuel  
•
Hofstetter, Albert  
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April 27, 2022
Journal Of The American Chemical Society

Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated with crystal structure prediction methods and density functional theory chemical shift calculations. Here, we successfully determine the crystal structures of ampicillin, piroxicam, cocaine, and two polymorphs of the drug molecule AZD8329 using on-the-fly generated machine-learned isotropic chemical shifts to directly guide a Monte Carlo-based structure determination process starting from a random gas-phase conformation.

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Type
research article
DOI
10.1021/jacs.1c13733
Web of Science ID

WOS:000799141600024

Author(s)
Balodis, Martins  
Cordova, Manuel  
Hofstetter, Albert  
Day, Graeme M.
Emsley, Lyndon  
Date Issued

2022-04-27

Published in
Journal Of The American Chemical Society
Volume

144

Issue

16

Start page

7215

End page

7223

Subjects

Chemistry, Multidisciplinary

•

Chemistry

•

structure prediction

•

nmr crystallography

•

powder crystallography

•

parameters

•

algorithm

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LRM  
FunderGrant Number

FNS

200020_178860

Available on Infoscience
June 6, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/188326
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