Among all possible NMR crystallography approaches for crystal-structure determination, crystal structure prediction – NMR crystallography (CSP-NMRX) has recently turned out to be a powerful method. In the latter, the original procedure exploited solid-state NMR (SSNMR) information during the final steps of the prediction. In particular, it used the comparison of computed and experimental chemical shifts for the selection of the correct crystal packing. Still, the prediction procedure, generally carried out with DFT methods, may require important computational resources and be quite time-consuming, especially if there are no available constraints to use at the initial stage. Herein, the successful application of this combined prediction method, which exploits NMR information also in the input step to reduce the search space of the predictive algorithm, is presented. Herein, this method was applied on mebendazole, which is characterized by desmotropism. The use of SSNMR data as constraints for the selection of the right tautomer and the determination of the number of independent molecules in the unit cell led to a considerably faster process, reducing the number of calculations to be performed. In this way, the crystal packing was successfully predicted for the three known phases of mebendazole. To evaluate the quality of the predicted structures, these were compared to the experimental ones. The crystal structure of phase B of mebendazole, in particular, was determined de novo by powder diffraction and is presented for the first time in this paper.

Solid-State NMR-Driven Crystal Structure Prediction of Molecular Crystals: The Case of Mebendazole

Bravetti F.;Bordignon S.;Nervi C.;Gobetto R.;Chierotti M. R.
2022-01-01

Abstract

Among all possible NMR crystallography approaches for crystal-structure determination, crystal structure prediction – NMR crystallography (CSP-NMRX) has recently turned out to be a powerful method. In the latter, the original procedure exploited solid-state NMR (SSNMR) information during the final steps of the prediction. In particular, it used the comparison of computed and experimental chemical shifts for the selection of the correct crystal packing. Still, the prediction procedure, generally carried out with DFT methods, may require important computational resources and be quite time-consuming, especially if there are no available constraints to use at the initial stage. Herein, the successful application of this combined prediction method, which exploits NMR information also in the input step to reduce the search space of the predictive algorithm, is presented. Herein, this method was applied on mebendazole, which is characterized by desmotropism. The use of SSNMR data as constraints for the selection of the right tautomer and the determination of the number of independent molecules in the unit cell led to a considerably faster process, reducing the number of calculations to be performed. In this way, the crystal packing was successfully predicted for the three known phases of mebendazole. To evaluate the quality of the predicted structures, these were compared to the experimental ones. The crystal structure of phase B of mebendazole, in particular, was determined de novo by powder diffraction and is presented for the first time in this paper.
2022
28
6
3589
3598
https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/chem.202103589
Bravetti F., Bordignon S., Alig E., Eisenbeil D., Fink L., Nervi C., Gobetto R., Schmidt M. U., Chierotti M. R.
File in questo prodotto:
File Dimensione Formato  
202103589.pdf

Accesso riservato

Descrizione: Articolo principale
Tipo di file: PDF EDITORIALE
Dimensione 2.44 MB
Formato Adobe PDF
2.44 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1837752
Citazioni
  • ???jsp.display-item.citation.pmc??? 9
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 16
social impact