Publication date: Sep 17, 2024
The recent development of ultra-fast MAS (>100 kHz) provides new opportunities for structural characterization in solids. Here we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1H resolution improvement at ultra-fast MAS and use solely 1H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13C-detected experiments, without the need to employ dynamic nuclear polarization.
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File name | Size | Description |
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verinurad_TopSpin_data.zip
MD5md5:179369f56a39d2f1c32c8a98e9d18047
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487.4 MiB | Raw NMR data of the experimental spectra measured. |
verinurad_pip_TopSpin_data.zip
MD5md5:e4b7342c4f2caddd1a98a8ffeaf4f69a
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3.3 MiB | Raw NMR data of the experimental spectra measured and used as input for PIPNet. |
Probabilistic_assignment.zip
MD5md5:11db59eb753daa27c3522025da5f8e1a
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1.0 MiB | The input files used for the Bayesian probabilistic assignment and the complete output data generated |
ShiftML2_predictions.zip
MD5md5:b3bd8089ea7d0eb8b31a1d5a32656ce3
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27.4 KiB | SXRD structure of verinurad with proton relaxed positions used as input for ShiftML2 and the Excel sheet showing the chemical shielding to chemical shift conversion |
GIPAW_calculations.zip
MD5md5:2ec088fd922d934d39462c55dfc47b33
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406.6 KiB | Input/output files for proton optimization and NMR calculation |
2024.136 (version v1) [This version] | Sep 17, 2024 | DOI10.24435/materialscloud:qk-x9 |