The program Mercury, developed at the Cambridge Crystallographic Data Centre, was originally designed primarily as a crystal structure visualization tool. Over the years the fields and scientific communities of chemical crystallography and crystal engineering have developed to require more advanced structural analysis software. Mercury has evolved alongside these scientific communities and is now a powerful analysis, design and prediction platform which goes a lot further than simple structure visualization.

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Mercury 4.0: from visualization to analysis, design and prediction

Solid-state nuclear magnetic resonance (NMR) spectroscopy is an atomic-level method used to determine the chemical structure, three-dimensional structure, and dynamics of solids and semi-solids. This Primer summarizes the basic principles of NMR as applied to the wide range of solid systems. The fundamental nuclear spin interactions and the effects of magnetic fields and radiofrequency pulses on nuclear spins are the same as in liquid-state NMR. However, because of the anisotropy of the interactions in the solid state, the majority of high-resolution solid-state NMR spectra is measured under magic-angle spinning (MAS), which has profound effects on the types of radiofrequency pulse sequences required to extract structural and dynamical information. We describe the most common MAS NMR experiments and data analysis approaches for investigating biological macromolecules, organic materials, and inorganic solids. Continuing development of sensitivity-enhancement approaches, including 1H-detected fast MAS experiments, dynamic nuclear polarization, and experiments tailored to ultrahigh magnetic fields, is described. We highlight recent applications of solid-state NMR to biological and materials chemistry. The Primer ends with a discussion of current limitations of NMR to study solids, and points to future avenues of development to further enhance the capabilities of this sophisticated spectroscopy for new applications.

Solid-state NMR spectroscopy

The calculation of chemical shifts in solids has enabled methods to determine crystal structures in powders. The dependence of chemical shifts on local atomic environments sets them among the most powerful tools for structure elucidation of powdered solids or amorphous materials. Unfortunately, this dependency comes with the cost of high accuracy first-principle calculations to qualitatively predict chemical shifts in solids. Machine learning methods have recently emerged as a way to overcome the need for explicit high accuracy first-principle calculations. However, the vast chemical and combinatorial space spanned by molecular solids, together with the strong dependency of chemical shifts of atoms on their environment, poses a huge challenge for any machine learning method. Here we propose a machine learning method based on local environments to accurately predict chemical shifts of different molecular solids and of different polymorphs within DFT accuracy (RMSE of 0.49 ppm ( 1 H), 4.3ppm ( 13 C), 13.3 ppm ( 15 N), and 17.7 ppm ( 17 O) with $R^2$ of 0.97 for 1 H, 0.99 for 13 C, 0.99 for 15 N, and 0.99 for 17 O). We also demonstrate that the trained model is able to correctly determine, based on the match between experimentally-measured and ML-predicted shifts, structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid in an chemical shift based NMR crystallography approach.

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Chemical Shifts in Molecular Solids by Machine Learning

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which  comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid. Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.

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Chemical shifts in molecular solids by machine learning

NMR crystallography of molecular organics.

By the combined use of powder and single-crystal X-ray diffraction, solid-state NMR, and molecular modeling, the crystal structures of two systems containing the unusually large tenapanor drug molecule have been determined: the free form, ANHY, and a dihydrochloride salt form, 2HCl. Dynamic nuclear polarization (DNP) assisted solid-state NMR (SSNMR) crystallography investigations were found essential for the final assignment and were used to validate the crystal structure of ANHY. From a structural informatics analysis of ANHY and 2HCl, conformational ring differences in one part of the molecule were observed which influence the relative orientation of a methyl group on a ring nitrogen and thereby impact the crystallizability of the dihydrochloride salt. From quantum chemistry calculations, the dynamics between different ring conformations in tenapanor is predicted to be fast. Addition of HCl to tenapanor results in general in a mixture of protonated ring conformers and hence a statistical mix of diastere...

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Elucidating an Amorphous Form Stabilization Mechanism for Tenapanor Hydrochloride: Crystal Structure Analysis Using X-ray Diffraction, NMR Crystallography, and Molecular Modeling

The crystal structure of the Form A polymorph of N-cyclopropyl-3-fluoro-4-methyl-5-[3-[[1-[2-[2-(methylamino)ethoxy]phenyl]cyclopropyl]amino]-2-oxo-pyrazin-1-yl]benzamide (ie, AZD7624), determined using single-crystal X-ray diffraction (scXRD) at 100 K, contains two molecules in the asymmetric unit (Z' = 2) and has regions of local static disorder This substance has been in phase IIa drug development trials for the treatment of chronic obstructive pulmonary disease, a disease which affects over 300 million people and contributes to nearly 3 million deaths annually While attempting to verify the crystal structure using nuclear magnetic resonance crystallography (NMRX), we measured 13C solid-state NMR (SSNMR) spectra at 295 K that appeared consistent with Z' = 1 rather than Z' = 2 To understand this surprising observation, we used multinuclear SSNMR (1H, 13C, 15N), gauge-including projector augmented-wave density functional theory (GIPAW DFT) calculations, crystal structure prediction (CSP), and powder XRD (pXRD) to determine the room temperature crystal structure Due to the large size of AZD7624 (ca 500 amu, 54 distinct 13C environments for Z' = 2), static disorder at 100 K, and (as we show) dynamic disorder at ambient temperatures, NMR spectral assignment was a challenge We introduce a method to enhance confidence in NMR assignments by comparing experimental 13C isotropic chemical shifts against site-specific DFT-calculated shift distributions established using CSP-generated crystal structures The assignment and room temperature NMRX structure determination process also included measurements of 13C shift tensors and the observation of residual dipolar coupling between 13C and 14N CSP generated ca 90 reasonable candidate structures (Z' = 1 and Z' = 2), which when coupled with GIPAW DFT results, room temperature pXRD, and the assigned SSNMR data, establish Z' = 2 at room temperature We find that the polymorphic Form A of AZD7624 is maintained at room temperature, although dynamic disorder is present on the NMR timescale Of the CSP-generated structures, 2 are found to be fully consistent with the SSNMR and pXRD data; within this pair, they are found to be structurally very similar (RMSD16 = 030 A) We establish that the CSP structure in best agreement with the NMR data possesses the highest degree of structural similarity with the scXRD-determined structure (RMSD16 = 017 A), and has the lowest DFT-calculated energy amongst all CSP-generated structures with Z' = 2

Does Z′ equal 1 or 2? Enhanced powder NMR crystallography verification of a disordered room temperature crystal structure of a p38 inhibitor for chronic obstructive pulmonary disease

Maria Lindkvist

Papers

Elucidating an Amorphous Form Stabilization Mechanism for Tenapanor Hydrochloride: Crystal Structure Analysis Using X-ray Diffraction, NMR Crystallography, and Molecular Modeling

Does Z′ equal 1 or 2? Enhanced powder NMR crystallography verification of a disordered room temperature crystal structure of a p38 inhibitor for chronic obstructive pulmonary disease