Powder crystallography by combined crystal structure prediction and high-resolution 1H solid-state NMR spectroscopy

Spiers Memorial Lecture: NMR crystallography

Chemical function is directly related to the spatial arrangement of atoms. Consequently, the determination of atomic-level three-dimensional structures has transformed molecular and materials science over the past 60 years. In this context, solid-state NMR has emerged to become the method of choice for atomic-level characterization of complex materials in powder form. In the following we present an overview of current methods for chemical shift driven NMR crystallography, illustrated with applications to complex materials.

Enhancing garlic propagation through functional biopolymer-based propagules coatings: A bio-nanotechnological strategy

Integrating agricultural, chemical, and technological knowledge is crucial for developing bio-nanotechnologies to improve agricultural production. This study explores the innovative use of biopolymeric coatings, based on sodium alginate and sodium alginate + Laponite® (nanoclay), containing biostimulants (tryptophol and thymol) or not, on garlic cloves. These coatings were analyzed using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR-ATR), and scanning electron microscopy (SEM). Greenhouse bioassays showed improvements in garlic shoot plant biomass with both treatments: sodium alginate biopolymer and sodium alginate biopolymer plus Laponite®. In the field experiment, garlic plants treated with sodium alginate, in combination with conventional pesticide treatments, resulted in better quality garlic bulbs, where larger garlics were harvested in this treatment, reducing commercial losses. In tropical garlic crops, obtaining plants with greater initial vigor is essential. Our results highlight the potential of these bio-nanotechnological strategies to enhance garlic propagation, ensuring environmental protection and food security.

Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning

The recent development of ultra-fast magic-angle spinning (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.

Atomic-level structure of the amorphous drug atuliflapon via NMR crystallography

We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions

Ab initio chemical shift prediction plays a central role in nuclear magnetic resonance (NMR) crystallography, and the accuracy with which chemical shifts can be predicted relative to experiment impacts the confidence with which structures can be assigned. For organic crystals, periodic density functional theory calculations with the gauge-including projector augmented wave (GIPAW) approximation and the PBE functional are widely used at present. Many previous studies have examined how using more advanced density functionals can increase the accuracy of predicted chemical shifts relative to experiment, but nearly all of those studies employed crystal structures that were optimized with generalized-gradient approximation (GGA) functionals. Here, we investigate how the accuracy of the predicted chemical shifts in organic crystals is affected by replacing GGA-level PBE-D3(BJ) crystal geometries with more accurate hybrid functional PBE0-D3(BJ) ones. Based on benchmark data sets containing 132 13C and 35 15N chemical shifts, plus case studies on testosterone, acetaminophen, and phenobarbital, we find that switching from GGA-level geometries and chemical shifts to hybrid-functional ones reduces 13C and 15N chemical shift errors by ∼40-60% versus experiment. However, most of the improvement stems from the use of the hybrid functional for the chemical shift calculations, rather than from the refined geometries. In addition, even with the improved geometries, we find that double-hybrid functionals still do not systematically increase chemical shift agreement with experiment beyond what hybrid functionals provide. In the end, these results suggest that the combination of GGA-level crystal structures and hybrid-functional chemical shifts represents a particularly cost-effective combination for NMR crystallography in organic systems.

Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data

The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures.

An unusual ionic cocrystal of ponatinib hydrochloride: characterization by single-crystal X-ray diffraction and ultra-high field NMR spectroscopy

This study describes the discovery of a unique ionic cocrystal of the active pharmaceutical ingredient (API) ponatinib hydrochloride (pon·HCl), and characterization using single-crystal X-ray diffraction (SCXRD) and solid-state NMR (SSNMR) spectroscopy. Pon·HCl is a multicomponent crystal that features an unusual stoichiometry, with an asymmetric unit containing both monocations and dications of the ponatinib molecule, three water molecules, and three chloride ions. Structural features include (i) a charged imidazopyridazine moiety that forms a hydrogen bond between the ponatinib monocations and dications and (ii) a chloride ion that does not feature hydrogen bonds involving any organic moiety, instead being situated in a "square" arrangement with three water molecules. Multinuclear SSNMR, featuring high and ultra-high fields up to 35.2 T, provides the groundwork for structural interpretation of complex multicomponent crystals in the absence of diffraction data. A 13C CP/MAS spectrum confirms the presence of two crystallographically distinct ponatinib molecules, whereas 1D 1H and 2D 1H-1H DQ-SQ spectra identify and assign the unusually deshielded imidazopyridazine proton. 1D 35Cl spectra obtained at multiple fields confirm the presence of three distinct chloride ions, with density functional theory calculations providing key relationships between the SSNMR spectra and H⋯Cl- hydrogen bonding arrangements. A 2D 35Cl → 1H D-RINEPT spectrum confirms the spatial proximities between the chloride ions, water molecules, and amine moieties. This all suggests future application of multinuclear SSNMR at high and ultra-high fields to the study of complex API solid forms for which SCXRD data are unavailable, with potential application to heterogeneous mixtures or amorphous solid dispersions.

High-temperature superconductivity up to 223 K in the Al stabilized metastable hexagonal lanthanum superhydride

As compressed hydrides constantly refresh the records of superconducting critical temperatures (Tc) in the vicinity of room temperature, this further reinforces the confidence to find more high-temperature superconducting hydrides. In this process, metastable phases of superhydrides offer enough possibilities to access superior superconducting properties. Here we report a metastable hexagonal lanthanum superhydride (P63/mmc-LaH10) stabilized at 146 GPa by introducing an appropriate proportion of Al, which exhibits high-temperature superconductivity with Tc ∼ 178 K, and this value is enhanced to a maximum Tc ∼ 223 K at 164 GPa. A huge upper critical magnetic field value Hc2(0) reaches 223 T at 146 GPa. The small volume expansion of P63/mmc-(La, Al) H10 compared with the binary LaH10 indicates the possible interstitial sites of Al atoms filling into the La-H lattice, instead of forming conventional ternary alloy-based superhydrides. This work provides a new strategy for metastable high-temperature superconductors through the multiple-element system.

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials

The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.

MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids

We developed a random forest machine learning (ML) model for the prediction of 1H and 13C NMR chemical shifts of nucleic acids. Our ML model is trained entirely on reproducing computed chemical shifts obtained previously on 10 nucleic acids using a Molecules-in-Molecules (MIM) fragment-based density functional theory (DFT) protocol including microsolvation effects. Our ML model includes structural descriptors as well as electronic descriptors from an inexpensive low-level semiempirical calculation (GFN2-xTB) and trained on a relatively small number of DFT chemical shifts (2080 1H chemical shifts and 1780 13C chemical shifts on the 10 nucleic acids). The ML model is then used to make chemical shift predictions on 8 new nucleic acids ranging in size from 600 to 900 atoms and compared directly to experimental data. Though no experimental data was used in the training, the performance of our model is excellent (mean absolute deviation of 0.34 ppm for 1H chemical shifts and 2.52 ppm for 13C chemical shifts for the test set), despite having some nonstandard structures. A simple analysis suggests that both structural and electronic descriptors are critical for achieving reliable predictions. This is the first attempt to combine ML from fragment-based DFT calculations to predict experimental chemical shifts accurately, making the MIM-ML model a valuable tool for NMR predictions of nucleic acids.

Atomic-level structure determination of amorphous molecular solids by NMR

Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder leads to a high degree of overlap between measured signals, and prevents the unambiguous identification of a single modeled periodic structure as representative of the whole material. Here, we determine the atomic-level ensemble structure of the amorphous form of the drug AZD4625 by combining solid-state NMR experiments with molecular dynamics (MD) simulations and machine-learned chemical shifts. By considering the combined shifts of all 1H and 13C atomic sites in the molecule, we determine the structure of the amorphous form by identifying an ensemble of local molecular environments that are in agreement with experiment. We then extract and analyze preferred conformations and intermolecular interactions in the amorphous sample in terms of the stabilization of the amorphous form of the drug.

Zwitterionic or Not? Fast and Reliable Structure Determination by Combining Crystal Structure Prediction and Solid-State NMR

When it comes to crystal structure determination, computational approaches such as Crystal Structure Prediction (CSP) have gained more and more attention since they offer some insight on how atoms and molecules are packed in the solid state, starting from only very basic information without diffraction data. Furthermore, it is well known that the coupling of CSP with solid-state NMR (SSNMR) greatly enhances the performance and the accuracy of the predictive method, leading to the so-called CSP-NMR crystallography (CSP-NMRX). In this paper, we present the successful application of CSP-NMRX to determine the crystal structure of three structural isomers of pyridine dicarboxylic acid, namely quinolinic, dipicolinic and dinicotinic acids, which can be in a zwitterionic form, or not, in the solid state. In a first step, mono- and bidimensional SSNMR spectra, i.e., ¹H Magic-Angle Spinning (MAS), ¹³C and ¹⁵N Cross Polarisation Magic-Angle Spinning (CPMAS), ¹H Double Quantum (DQ) MAS, ¹H-¹³C HETeronuclear CORrelation (HETCOR), were used to determine the correct molecular structure (i.e., zwitterionic or not) and the local molecular arrangement; at the end, the RMSEs between experimental and computed ¹H and ¹³C chemical shifts allowed the selection of the correct predicted structure for each system. Interestingly, while quinolinic and dipicolinic acids are zwitterionic and non-zwitterionic, respectively, in the solid state, dinicotinic acid exhibits in its crystal structure a "zwitterionic-non-zwitterionic continuum state" in which the proton is shared between the carboxylic moiety and the pyridinic nitrogen. Very refined SSNMR experiments were carried out, i.e., ¹⁴N-¹H Phase-Modulated (PM) pulse and Rotational-Echo Saturation-Pulse Double-Resonance (RESPDOR), to provide an accurate N-H distance value confirming the hybrid nature of the molecule. The CSP-NMRX method showed a remarkable match between the selected structures and the experimental ones. The correct molecular input provided by SSNMR reduced the number of CSP calculations to be performed, leading to different predicted structures, while RMSEs provided an independent parameter with respect to the computed energy for the selection of the best candidate.

Pure Isotropic Proton NMR Spectra in Solids using Deep Learning

The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution 1 H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.

Ultrafast Magic Angle Spinning Solid-State NMR Spectroscopy: Advances in Methodology and Applications

Solid-state NMR spectroscopy is one of the most commonly used techniques to study the atomic-resolution structure and dynamics of various chemical, biological, material, and pharmaceutical systems spanning multiple forms, including crystalline, liquid crystalline, fibrous, and amorphous states. Despite the unique advantages of solid-state NMR spectroscopy, its poor spectral resolution and sensitivity have severely limited the scope of this technique. Fortunately, the recent developments in probe technology that mechanically rotate the sample fast (100 kHz and above) to obtain "solution-like" NMR spectra of solids with higher resolution and sensitivity have opened numerous avenues for the development of novel NMR techniques and their applications to study a plethora of solids including globular and membrane-associated proteins, self-assembled protein aggregates such as amyloid fibers, RNA, viral assemblies, polymorphic pharmaceuticals, metal-organic framework, bone materials, and inorganic materials. While the ultrafast-MAS continues to be developed, the minute sample quantity and radio frequency requirements, shorter recycle delays enabling fast data acquisition, the feasibility of employing proton detection, enhancement in proton spectral resolution and polarization transfer efficiency, and high sensitivity per unit sample are some of the remarkable benefits of the ultrafast-MAS technology as demonstrated by the reported studies in the literature. Although the very low sample volume and very high RF power could be limitations for some of the systems, the advantages have spurred solid-state NMR investigation into increasingly complex biological and material systems. As ultrafast-MAS NMR techniques are increasingly used in multidisciplinary research areas, further development of instrumentation, probes, and advanced methods are pursued in parallel to overcome the limitations and challenges for widespread applications. This review article is focused on providing timely comprehensive coverage of the major developments on instrumentation, theory, techniques, applications, limitations, and future scope of ultrafast-MAS technology.

Deconvolution of metal apportionment in bulk metal-organic frameworks

We report a general route to decipher the apportionment of metal ions in bulk metal-organic frameworks (MOFs) by solid-state nuclear magnetic resonance spectroscopy. We demonstrate this route in Mg_1-xNi_x-MOF-74, where we uncover all eight possible atomic-scale Mg/Ni arrangements through identification and quantification of the distinct chemical environments of ¹³C-labeled carboxylates as a function of the Ni content. Here, we use magnetic susceptibility, bond pathway, and density functional theory calculations to identify local metal bonding configurations. The results refute the notion of random apportionment from solution synthesis; rather, we reveal that only two of eight Mg/Ni arrangements are preferred in the Ni-incorporated MOFs. These preferred structural arrangements manifest themselves in macroscopic adsorption phenomena as illustrated by CO/CO₂ breakthrough curves. We envision that this nondestructive methodology can be further applied to analyze bulk assembly of other mixed-metal MOFs, greatly extending the knowledge on structure-property relationships of MOFs and their derived materials.

Narrowing down the conformational space with solid-state NMR in crystal structure prediction of linezolid cocrystals

Many solids crystallize as microcrystalline powders, thus precluding the application of single crystal X-Ray diffraction in structural elucidation. In such cases, a joint use of high-resolution solid-state NMR and crystal structure prediction (CSP) calculations can be successful. However, for molecules showing significant conformational freedom, the CSP-NMR protocol can meet serious obstacles, including ambiguities in NMR signal assignment and too wide conformational search space to be covered by computational methods in reasonable time. Here, we demonstrate a possible way of avoiding these obstacles and making as much use of the two methods as possible in difficult circumstances. In a simple case, our experiments led to crystal structure elucidation of a cocrystal of linezolid (LIN), a wide-range antibiotic, with 2,3-dihydroxybenzoic acid, while a significantly more challenging case of a cocrystal of LIN with 2,4-dihydroxybenzoic acid led to the identification of the most probable conformations of LIN inside the crystal. Having four rotatable bonds, some of which can assume many discreet values, LIN molecule poses a challenge in establishing its conformation in a solid phase. In our work, a set of 27 conformations were used in CSP calculations to yield model crystal structures to be examined against experimental solid-state NMR data, leading to a reliable identification of the most probable molecular arrangements.

Combining X-ray and NMR Crystallography to Explore the Crystallographic Disorder in Salbutamol Oxalate

Salbutamol is an active pharmaceutical ingredient commonly used to treat respiratory distress and is listed by the World Health Organization as an essential medicine. Here, we establish the crystal structure of its oxalate form, salbutamol oxalate, and explore the nature of its crystallographic disorder by combined X-ray crystallography and ¹³C cross-polarization (CP) magic-angle spinning (MAS) solid-state NMR. The *C-OH chiral center of salbutamol (note that the crystal structures are a racemic mixture of the two enantiomers of salbutamol) is disordered over two positions, and the tert-butyl group is rotating rapidly, as revealed by ¹³C solid-state NMR. The impact of crystallization conditions on the disorder was investigated, finding variations in the occupancy ratio of the *C-OH chiral center between single crystals and a consistency across samples in the bulk powder. Overall, this work highlights the contrast between investigating crystallographic disorder by X-ray diffraction and solid-state NMR experiment, and gauge-including projector-augmented-wave (GIPAW) density functional theory (DFT) calculations, with their combined use, yielding an improved understanding of the nature of the crystallographic disorder between the local (i.e., as viewed by NMR) and longer-range periodic (i.e., as viewed by diffraction) scale.

19F Solid-state NMR characterization of pharmaceutical solids

Solid-state NMR has been increasingly recognized as a high-resolution and versatile spectroscopic tool to characterize drug substances and products. However, the analysis of pharmaceutical materials is often carried out at natural isotopic abundance and a relatively low drug loading in multi-component systems and therefore suffers from challenges of low sensitivity. The fact that fluorinated therapeutics are well represented in pipeline drugs and commercial products offers an excellent opportunity to utilize fluorine as a molecular probe for pharmaceutical analysis. We aim to review recent advancements of ¹⁹F magic angle spinning NMR methods in modern drug research and development. Applications to polymorph screening at the micromolar level, structural elucidation, and investigation of molecular interactions at the Ångström to submicron resolution in drug delivery, stability, and quality will be discussed.

Combining heteronuclear correlation NMR with spin-diffusion to detect relayed Cl-H-H and N-H-H proximities in molecular solids

Analysis of short-to-intermediate range intermolecular interactions offers a great way of characterizing the solid-state organization of small molecules and materials. This can be achieved by two-dimensional (2D) homo- and heteronuclear correlation NMR spectroscopy, for example, by carrying out experiments at high magnetic fields in conjunction with fast magic-angle spinning (MAS) techniques. But, detecting 2D peaks for heteronuclear dipolar coupled spin pairs separated by greater than 3 Å is not always straightforward, particularly when low-gamma quadrupolar nuclei are involved. Here, we present a 2D correlation NMR experiment that combines the advantages of heteronuclear-multiple quantum coherence (HMQC) and proton-based spin-diffusion (SD) pulse sequences using radio-frequency-driven-recouping (RFDR) to probe inter and intramolecular ¹H-X (X = ¹⁴N, ³⁵Cl) interactions. This experiment can be used to acquire 2D ¹H{X}-HMQC filtered ¹H-¹H correlation as well as 2D ¹H-X HMQC spectra. Powder forms of dopamine·HCl and l-histidine·HCl·H₂O are characterized at high fields (21.1 T and 18.8 T) with fast MAS (60 kHz) using the 2D HMQC-SD-RFDR approach. Solid-state NMR results are complemented with NMR crystallography analyses using the gauge-including projector augmented wave (GIPAW) approach. For histidine·HCl·H₂O, 2D peaks associated with ¹⁴N-¹H-¹H and ³⁵Cl-¹H-¹H distances of up to 4.4 and 3.9 Å have been detected. This is further corroborated by the observation of 2D peaks corresponding to ¹⁴N-¹H-¹H and ³⁵Cl-¹H-¹H distances of up to 4.2 and 3.7 Å in dopamine·HCl, indicating the suitability of the HMQC-SD-RFDR experiments for detecting medium-range proximities in molecular solids.

Ambiguous structure determination from powder data: four different structural models of 4,11-di-fluoro-quinacridone with similar X-ray powder patterns, fit to the PDF, SSNMR and DFT-D

Four different structural models, which all fit the same X-ray powder pattern, were obtained in the structure determination of 4,11-di-fluoro-quinacridone (C20H10N2O2F2) from unindexed X-ray powder data by a global fit. The models differ in their lattice parameters, space groups, Z, Z', molecular packing and hydrogen bond patterns. The molecules form a criss-cross pattern in models A and B, a layer structure built from chains in model C and a criss-cross arrangement of dimers in model D. Nevertheless, all models give a good Rietveld fit to the experimental powder pattern with acceptable R-values. All molecular geometries are reliable, except for model D, which is slightly distorted. All structures are crystallochemically plausible, concerning density, hydrogen bonds, intermolecular distances etc. All models passed the checkCIF test without major problems; only in model A a missed symmetry was detected. All structures could have probably been published, although 3 of the 4 structures were wrong. The investigation, which of the four structures is actually the correct one, was challenging. Six methods were used: (1) Rietveld refinements, (2) fit of the crystal structures to the pair distribution function (PDF) including the refinement of lattice parameters and atomic coordinates, (3) evaluation of the colour, (4) lattice-energy minimizations with force fields, (5) lattice-energy minimizations by two dispersion-corrected density functional theory methods, and (6) multinuclear CPMAS solid-state NMR spectroscopy (1H, 13C, 19F) including the comparison of calculated and experimental chemical shifts. All in all, model B (perhaps with some disorder) can probably be considered to be the correct one. This work shows that a structure determination from limited-quality powder data may result in totally different structural models, which all may be correct or wrong, even if they are chemically sensible and give a good Rietveld refinement. Additionally, the work is an excellent example that the refinement of an organic crystal structure can be successfully performed by a fit to the PDF, and the combination of computed and experimental solid-state NMR chemical shifts can provide further information for the selection of the most reliable structure among several possibilities.

Complete resonance assignment of a pharmaceutical drug at natural isotopic abundance from DNP-Enhanced solid-state NMR

Solid-state dynamic nuclear polarization enhanced magic angle spinning (DNP-MAS) NMR measurements coupled with density functional theory (DFT) calculations enable the full resonance assignment of a complex pharmaceutical drug molecule without the need for isotopic enrichment. DNP dramatically enhances the NMR signals, thereby making possible previously intractable two-dimensional correlation NMR spectra at natural abundance. Using inputs from DFT calculations, herein we describe a significant improvement to the structure elucidation process for complex organic molecules. Further, we demonstrate that a series of two-dimensional correlation experiments, including ¹⁵N-¹³C TEDOR, ¹³C-¹³C INADEQUATE/SARCOSY, ¹⁹F-¹³C HETCOR, and ¹H-¹³C HETCOR, can be obtained at natural isotopic abundance within reasonable experiment times, thus enabling a complete resonance assignment of sitagliptin, a pharmaceutical used for the treatment of type 2 diabetes.

De Novo Crystal Structure Determination from Machine Learned Chemical Shifts

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.

Resolving alternative structure determinations of indapamide using 13C solid-state NMR

The conflict between alternative crystal structures in the Cambridge Structural Database for the diuretic drug indapamide hemihydrate (IND) has been resolved with the aid of ¹³C solid-state NMR. IND is seen to contain multiple distinct molecules in the asymmetric unit (Z' = 4) rather than exhibiting disorder in the orientation of sulfonamide groups. The NMR crystallographic approach is a more effective tool for distinguishing between alternative structures than naïve judgements of quality based on crystallographic refinement agreement factors.

Along the road to Crystal Structure Prediction (CSP) of pharmaceutical-like molecules

Computational methods used for predicting crystal structures of organic compounds are mature enough to be routinely used with many rigid and semi-rigid organic molecules. The usefulness of Crystal Structure Prediction...

A combination of Proton Spin Diffusion NMR and molecular simulations to probe supramolecular assemblies of organic molecules in nanoporous materials

In this work we show the use of high-resolution 1H MAS NMR to distinguish between two kinds of aggregation states of (1R,2S)-ephedrine, a chiral organic structure directing agent, occluded within...

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

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.

A toolbox for improving the workflow of NMR crystallography

NMR crystallography is a powerful tool with applications in structural characterization and crystal structure verification, to name two. However, applying this tool presents several challenges, especially for industrial users, in terms of consistency, workflow, time consumption, and the requirement for a high level of understanding of experimental solid-state NMR and GIPAW-DFT calculations. Here, we have developed a series of fully parameterized scripts for use in Materials Studio and TopSpin, based on the .magres file format, with a focus on organic molecules (e.g. pharmaceuticals), improving efficiency, robustness, and workflow. We separate these tools into three major categories: performing the DFT calculations, extracting & visualizing the results, and crystallographic modelling. These scripts will rapidly submit fully parameterized CASTEP jobs, extract data from the calculations, assist in visualizing the results, and expedite the process of structural modelling. Accompanied with these tools is a description on their functionality, documentation on how to get started and use the scripts, and links to video tutorials for guiding new users. Through the use of these tools, we hope to facilitate NMR crystallography and to harmonize the process across users.

Bayesian probabilistic assignment of chemical shifts in organic solids

A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the ¹³C and ¹H assignment of 11 molecular solids with experimental shifts and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases.

Quantum Mechanical-Based Stability Evaluation of Crystal Structures for HIV-Targeted Drug Cabotegravir

The long-acting parenteral formulation of the HIV integrase inhibitor cabotegravir (GSK744) is currently being developed to prevent HIV infections, benefiting from infrequent dosing and high efficacy. The crystal structure can affect the bioavailability and efficacy of cabotegravir. However, the stability determination of crystal structures of GSK744 have remained a challenge. Here, we introduced an ab initio protocol to determine the stability of the crystal structures of pharmaceutical molecules, which were obtained from crystal structure prediction process starting from the molecular diagram. Using GSK744 as a case study, the ab initio predicted that Gibbs free energy provides reliable further refinement of the predicted crystal structures and presents its capability for becoming a crystal stability determination approach in the future. The proposed work can assist in the comprehensive screening of pharmaceutical design and can provide structural predictions and stability evaluation for pharmaceutical crystals.

Importance of Nuclear Quantum Effects for NMR Crystallography

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy. To test the limits of this approximation, we systematically study the importance of finite-temperature and quantum nuclear fluctuations for ¹H, ¹³C, and ¹⁵N shieldings in polymorphs of three paradigmatic molecular crystals: benzene, glycine, and succinic acid. The effect of quantum fluctuations is comparable to the typical errors of shielding predictions for static nuclei with respect to experiments, and their inclusion improves the agreement with measurements, translating to more reliable assignment of the NMR spectra to the correct candidate structure. The use of integrated machine-learning models, trained on first-principles energies and shieldings, renders rigorous sampling of nuclear fluctuations affordable, setting a new standard for the calculations underlying NMR structure determinations.

Accurate fragment-based 51-V chemical shift predictions in molecular crystals

Nuclear magnetic resonance (NMR) spectroscopy plays a crucial role in determining molecular structure for complex biological and pharmaceutical compounds. NMR investigations are increasingly reliant on computation for mapping spectral features to chemical structures. Here we benchmark the accuracy of fragment-based ⁵¹V chemical shielding tensor calculations using a training set comprised of 10 biologically and pharmaceutically relevant oxovanadium complexes. Using our self-consistent reproduction of the Madelung potential (SCRMP) electrostatic embedding model, we demonstrate comparable performance between fragment methods and computationally demanding cluster-based techniques. Specifically, fragment methods employing hybrid density functionals are capable of reproducing the experimental ⁵¹V isotropic chemical shifts with a training set rms error of ~9 ​ppm, representing a 20% improvement over traditional plane wave techniques. We provide training set-derived linear regression models for mapping the absolute shieldings obtained from computation to the experimentally determined chemical shifts using four common density functionals; PBE0, B3LYP, PBE, and BLYP. Finally, we establish the utility of fragment methods and the reported regression parameters examining four oxovanadium structures excluded from the training set including the tetracoordinate oxovanadium silicate [Formula: see text] , VO¹⁵NGlySalbz which contains redox-active ligands, and the solid-state form of the common ⁵¹V NMR reference compound VOCl₃.

Structural Refinement of Carbimazole by NMR Crystallography

The characterization of the three-dimensional structure of solids is of major importance, especially in the pharmaceutical field. In the present work, NMR crystallography methods are applied with the aim to refine the crystal structure of carbimazole, an active pharmaceutical ingredient used for the treatment of hyperthyroidism and Grave’s disease. Starting from previously reported X-ray diffraction data, two refined structures were obtained by geometry optimization methods. Experimental ¹H and ¹³C isotropic chemical shift measured by the suitable ¹H and ¹³C high-resolution solid state NMR techniques were compared with DFT-GIPAW calculated values, allowing the quality of the obtained structure to be experimentally checked. The refined structure was further validated through the analysis of ¹H-¹H and ¹H-¹³C 2D NMR correlation experiments. The final structure differs from that previously obtained from X-ray diffraction data mostly for the position of hydrogen atoms.

Pure Isotropic Proton Solid State NMR

Resolution in proton solid state magic angle sample spinning (MAS) NMR is limited by the intrinsically imperfect nature of coherent averaging induced by either MAS or multiple pulse sequence methods. Here, we suggest that instead of optimizing and perfecting a coherent averaging scheme, we could approach the problem by parametrically mapping the error terms due to imperfect averaging in a k-space representation, in such a way that they can be removed in a multidimensional correlation leaving only the desired pure isotropic signal. We illustrate the approach here by determining pure isotropic ¹H spectra from a series of MAS spectra acquired at different spinning rates. For six different organic solids, the approach is shown to produce pure isotropic ¹H spectra that are significantly narrower than the MAS spectrum acquired at the fastest possible rate, with linewidths down to as little as 48 Hz. On average, we observe a 7-fold increase in resolution, and up to a factor of 20, as compared with spectra acquired at 100 kHz MAS. The approach is directly applicable to a range of solids, and we anticipate that the same underlying principle for removing errors introduced here can be applied to other problems in NMR spectroscopy.

Synergy of Solid-State NMR, Single-Crystal X-ray Diffraction, and Crystal Structure Prediction Methods: A Case Study of Teriflunomide (TFM)

In this work, for the first time, we present the X-ray diffraction crystal structure and spectral properties of a new, room-temperature polymorph of teriflunomide (TFM), CSD code 1969989. As revealed by DSC, the low-temperature TFM polymorph recently reported by Gunnam et al. undergoes a reversible thermal transition at -40 °C. This reversible process is related to a change in Z' value, from 2 to 1, as observed by variable-temperature ¹H-¹³C cross-polarization (CP) magic-angle spinning (MAS) solid-state NMR, while the crystallographic system is preserved (triclinic). Two-dimensional ¹³C-¹H and ¹H-¹H double-quantum MAS NMR spectra are consistent with the new room-temperature structure, including comparison with GIPAW (gauge-including projector augmented waves) calculated NMR chemical shifts. A crystal structure prediction procedure found both experimental teriflunomide polymorphs in the energetic global minimum region. Differences between the polymorphs are seen for the torsional angle describing the orientation of the phenyl ring relative to the planarity of the TFM molecule. In the low-temperature structure, there are two torsion angles of 4.5 and 31.9° for the two Z' = 2 molecules, while in the room-temperature structure, there is disorder that is modeled with ∼50% occupancy between torsion angles of -7.8 and 28.6°. These observations are consistent with a broad energy minimum as revealed by DFT calculations. PISEMA solid-state NMR experiments show a reduction in the C-H dipolar coupling in comparison to the static limit for the aromatic CH moieties of 75% and 51% at 20 and 40 °C, respectively, that is indicative of ring flips at the higher temperature. Our study shows the power of combining experiments, namely DSC, X-ray diffraction, and MAS NMR, with DFT calculations and CSP to probe and understand the solid-state landscape, and in particular the role of dynamics, for pharmaceutical molecules.

Bridging photochemistry and photomechanics with NMR crystallography: the molecular basis for the macroscopic expansion of an anthracene ester nanorod

Crystals composed of photoreactive molecules represent a new class of photomechanical materials with the potential to generate large forces on fast timescales. An example is the photodimerization of 9-tert-butyl-anthracene ester (9TBAE) in molecular crystal nanorods that leads to an average elongation of 8%. Previous work showed that this expansion results from the formation of a metastable crystalline product. In this article, it is shown how a novel combination of ensemble oriented-crystal solid-state NMR, X-ray diffraction, and first principles computational modeling can be used to establish the absolute unit cell orientations relative to the shape change, revealing the atomic-resolution mechanism for the photomechanical response and enabling the construction of a model that predicts an elongation of 7.4%, in good agreement with the experimental value. According to this model, the nanorod expansion does not result from an overall change in the volume of the unit cell, but rather from an anisotropic rearrangement of the molecular contents. The ability to understand quantitatively how molecular-level photochemistry generates mechanical displacements allows us to predict that the expansion could be tuned from +9% to -9.5% by controlling the initial orientation of the unit cell with respect to the nanorod axis. This application of NMR-assisted crystallography provides a new tool capable of tying the atomic-level structural rearrangement of the reacting molecular species to the mechanical response of a nanostructured sample.

Towards Accurate Predictions of Proton NMR Spectroscopic Parameters in Molecular Solids

The factors contributing to the accuracy of quantum-chemical calculations for the prediction of proton NMR chemical shifts in molecular solids are systematically investigated. Proton chemical shifts of six solid amino acids with hydrogen atoms in various bonding environments (CH, CH₂ , CH₃ , OH, SH and NH₃ ) were determined experimentally using ultra-fast magic-angle spinning and proton-detected 2D NMR experiments. The standard DFT method commonly used for the calculations of NMR parameters of solids is shown to provide chemical shifts that deviate from experiment by up to 1.5 ppm. The effects of the computational level (hybrid DFT functional, coupled-cluster calculation, inclusion of relativistic spin-orbit coupling) are thoroughly discussed. The effect of molecular dynamics and nuclear quantum effects are investigated using path-integral molecular dynamics (PIMD) simulations. It is demonstrated that the accuracy of the calculated proton chemical shifts is significantly better when these effects are included in the calculations.

Application of NMR Crystallography to Highly Disordered Templated Materials: Extensive Local Structural Disorder in the Gallophosphate GaPO-34A

We present an NMR crystallographic investigation of two as-made forms of the recently characterized gallophosphate GaPO-34A, which has an unusual framework composition with a Ga:P ratio of 7:6 and contains both hydroxide and fluoride anions and either 1-methylimidazolium or pyridinium as the structure-directing agent. We combine previously reported X-ray crystallographic data with solid-state NMR spectroscopy and periodic density functional theory (DFT) calculations to show that the structure contains at least three distinct types of disorder (occupational, compositional, and dynamic). The occupational disorder arises from the presence of six anion sites per unit cell, but a total occupancy of five of these, leading to full occupancy of four sites and partial occupancy of the fifth and sixth (which are related by symmetry). The mixture of OH and F present leads to compositional disorder on the occupied anion sites, although the occupancy of some sites by F is calculated to be energetically unfavorable and signals relating to F on these sites are not observed by NMR spectroscopy, confirming that the compositional disorder is not random. Finally, a combination of high-field ⁷¹Ga NMR spectroscopy and variable-temperature ¹³C and ³¹P NMR experiments shows that the structure directing agents are dynamic on the microsecond time scale, which can be supported by averaging the ³¹P chemical shifts calculated with the SDA in different orientations. This demonstrates the value of an NMR crystallographic approach, particularly in the case of highly disordered crystalline materials, where the growth of large single crystals for conventional structure determination may not be possible owing to the extent of disorder present.

Molecular Mechanism of Crystalline-to-Amorphous Conversion of Pharmaceutical Solids from 19F Magic Angle Spinning NMR

Crystalline and amorphous materials usually possess distinct physicochemical properties due to major variations in long-range and local molecular packings. Enhanced fundamental knowledge of the molecular details of crystalline-to-amorphous interconversions is necessary to correlate the intermolecular structure to material properties and functions. While crystal structures can be readily obtained by X-ray crystallography, the microstructure of amorphous materials has rarely been explored due to a lack of high-resolution techniques capable of probing local molecular structures. Moreover, there is increasing interest in understanding the molecular nature of amorphous solids in pharmaceutical sciences due to the widespread utilization of amorphous active pharmaceutical ingredients (APIs) in pharmaceutical development for solubility and bioavailability enhancement. In this study, we explore multidimensional ¹³C and ¹⁹F magic angle spinning (MAS) NMR spectroscopy to study the molecular packing of amorphous posaconazole (POSA) in conjunction with the crystalline counterpart. Utilizing methods integrating homonuclear and heteronuclear ¹H, ¹³C, and ¹⁹F correlation spectroscopy and atomic ¹⁹F-to-¹³C distance measurements, we identified the major differences in molecular packing between crystalline and amorphous POSA. The intermolecular "head-to-head" interaction along the molecule's major axis, as well as the "head-to-tail" molecular packing perpendicular to the major axis in POSA crystals, was recapitulated by MAS NMR. Furthermore, critical intermolecular distances in the crystal lattice were determined. Most importantly, the head-to-tail contact of two neighboring molecules was found to be preserved in amorphous POSA, suggesting localized molecular order, whereas crucial interactions for head-to-head packing are absent in the amorphous form resulting in long-range disorder. Our study, likely one of the first documented examples, provides molecular-level structural details to understand the molecular mechanism of crystalline-to-amorphous conversion of fluorine-containing drug substances occurring in drug processing and development and establish a high-resolution experimental protocol for investigating amorphous materials.

NMR crystallography of molecular organics

Developments of NMR methodology to characterise the structures of molecular organic structures are reviewed, concentrating on the previous decade of research in which density functional theory-based calculations of NMR parameters in periodic solids have become widespread. With a focus on demonstrating the new structural insights provided, it is shown how "NMR crystallography" has been used in a spectrum of applications from resolving ambiguities in diffraction-derived structures (such as hydrogen atom positioning) to deriving complete structures in the absence of diffraction data. As well as comprehensively reviewing applications, the different aspects of the experimental and computational techniques used in NMR crystallography are surveyed. NMR crystallography is seen to be a rapidly maturing subject area that is increasingly appreciated by the wider crystallographic community.

Resolving alternative organic crystal structures using density functional theory and NMR chemical shifts

Alternative (‘repeat’) determinations of organic crystal structures deposited in the Cambridge Structural Database are analysed to characterise the nature and magnitude of the differences between structure solutions obtained by diffraction methods. Of the 3132 structure pairs considered, over 20% exhibited local structural differences exceeding 0.25 Å. In most cases (about 83%), structural optimisation using density functional theory (DFT) resolved the differences. Many of the cases where distinct and chemically significant structural differences remained after optimisation involved differently positioned hydroxyl groups, with obvious implications for the correct description of hydrogen bonding. ¹H and ¹³C chemical shifts from solid-state NMR experiments are proposed as an independent methodology in cases where DFT optimisation fails to resolve discrepancies.

DFT optimisation often resolves conflicting crystal structure determinations, with NMR shifts helping in cases where optimisation diverges to different structures.

Molecular packing of pharmaceuticals analyzed with paramagnetic relaxation enhancement and ultrafast magic angle pinning NMR

Probing molecular details of fluorinated pharmaceutical compounds at a faster acquisition utilizing paramagnetic relaxation enhancement and better resolution from ultrafast magic angle spinning (ν_rot = 110 kHz) and high magnetic field (B₀ = 18.8 T).

Polar surface structure of oxide nanocrystals revealed with solid-state NMR spectroscopy

Compared to nanomaterials exposing nonpolar facets, polar-faceted nanocrystals often exhibit unexpected and interesting properties. The electrostatic instability arising from the intrinsic dipole moments of polar facets, however, leads to different surface configurations in many cases, making it challenging to extract detailed structural information and develop structure-property relations. The widely used electron microscopy techniques are limited because the volumes sampled may not be representative, and they provide little chemical bonding information with low contrast of light elements. With ceria nanocubes exposing (100) facets as an example, here we show that the polar surface structure of oxide nanocrystals can be investigated by applying 17O and 1H solid-state NMR spectroscopy and dynamic nuclear polarization, combined with DFT calculations. Both CeO4-termination reconstructions and hydroxyls are present for surface polarity compensation and their concentrations can be quantified. These results open up new possibilities for investigating the structure and properties of oxide nanostructures with polar facets.

A Bayesian approach to NMR crystal structure determination

Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning. However, the reliability of the determination depends on the errors in the predicted shifts. Here we propose a Bayesian framework for determining the confidence in the identification of the experimental crystal structure, based on knowledge of the typical errors in the electronic structure methods. We demonstrate the approach on the determination of the structures of six organic molecular crystals. We critically assess the reliability of the structure determinations, facilitated by the introduction of a visualization of the similarity between candidate configurations in terms of their chemical shifts and their structures. We also show that the commonly used values for the errors in calculated ¹³C shifts are underestimated, and that more accurate, self-consistently determined uncertainties make it possible to use ¹³C shifts to improve the accuracy of structure determinations. Finally, we extend the recently-developed ShiftML model to render it more efficient, accurate, and, most importantly, to evaluate the uncertainties in its predictions. By quantifying the confidence in structure determinations based on ShiftML predictions we further substantiate that it provides a valid replacement for first-principles calculations in NMR crystallography.

Improving Predicted Nuclear Magnetic Resonance Chemical Shifts Using the Quasi-Harmonic Approximation

Ab initio nuclear magnetic resonance chemical shift prediction plays an important role in the determination or validation of crystal structures. The ability to predict chemical shifts more accurately can translate to increased confidence in the resulting chemical shift or structural assignments. Standard electronic structure predictions for molecular crystal structures neglect thermal expansion, which can lead to an appreciable underestimation of the molar volumes. This study examines this volume error and its impact on 68 ¹³C- and 28 ¹⁵N-predicted chemical shifts taken from 20 molecular crystals. It assesses the ability to recover more realistic room-temperature crystal structures using the quasi-harmonic approximation and how refining the structures impacts the chemical shifts. Several pharmaceutical molecular crystals are also examined in more detail. On the whole, accounting for quasi-harmonic expansion changes the ¹³C and ¹⁵N chemical shifts by 0.5 and 1.0 ppm on average. This, in turn, reduces the root-mean-square errors relative to experiment by 0.3 ppm for ¹³C and 0.7 ppm for ¹⁵N. Although the statistical impacts are modest, changes in individual chemical shifts can reach multiple ppm. Accounting for thermal expansion in molecular crystal chemical shift prediction may not be needed routinely, but the systematic trend toward improved accuracy with the experiment could be useful in cases where discrimination between structural candidates is challenging, as in the pharmaceutical theophylline.