Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions

Chemical shift prediction plays an
important role in the determination
or validation of crystal structures with solid-state nuclear magnetic
resonance (NMR) spectroscopy. One of the fundamental theoretical challenges
lies in discriminating variations in chemical shifts resulting from
different crystallographic environments. Fragment-based electronic
structure methods provide an alternative to the widely used plane
wave gauge-including projector augmented wave (GIPAW) density functional
technique for chemical shift prediction. Fragment methods allow hybrid
density functionals to be employed routinely in chemical shift prediction,
and we have recently demonstrated appreciable improvements in the
accuracy of the predicted shifts when using the hybrid PBE0 functional
instead of generalized gradient approximation (GGA) functionals like
PBE. Here, we investigate the solid-state 13C and 15N NMR spectra for multiple crystal forms of acetaminophen,
phenobarbital, and testosterone. We demonstrate that the use of the
hybrid density functional instead of a GGA provides both higher accuracy
in the chemical shifts and increased discrimination among the different
crystallographic environments. Finally, these results also provide
compelling evidence for the transferability of the linear regression
parameters mapping predicted chemical shieldings to chemical shifts
that were derived in an earlier study.