Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration

Selective crystallization of pyrazinamide polymorphs in supramolecular gels: Synergistic selectivity by mimetic gelator and solvent

A mimetic gelator designed to incorporate the chemical structure of pyrazinamide (PZA), a highly polymorphic drug, has been synthesized. Metastable Forms β and δ of PZA were obtained from supramolecular gel phase crystallization in nitrobenzene and DMSO, respectively, using a bis(urea) gelator designed to mimic the structure of PZA. This is the only known way to access the pure Form β at room temperature. In contrast, concomitant crystallization of a mixture of metastable polymorphs and the most thermodynamically stable form were obtained from solution crystallization. By analyzing the intermolecular interactions of PZA in the mimetic gel phase crystallization, it is proposed that the mimetic gelator and solvent can influence the nucleation behavior by close interaction with the carbonyl group to select PZA Forms β and δ.

Discovery of the most stable form of an adenosine receptor antagonist through virtual polymorph screening and targeted crystallization

An innovative approach was developed to identify the optimal crystalline form, usually the thermodynamically most stable form. This method involves using virtual polymorph screening and targeted crystallization based on in silico solid-state modeling. By utilizing advanced crystal structure prediction (CSP) technology, the virtual polymorph screening method helps confirm whether the most stable crystalline form has been identified in actual crystallization experiments. If the predicted most stable form is not observed in experiments, predictions based on the method of COnductor like Screening MOdel for Real Solvents (COSMO-RS) are used to highlight solvent systems that can increase the likelihood of experimentally obtaining the desired form through a targeted crystallization process. In this work, such an approach has enabled the rapid discovery of the most stable polymorphic form and the development of a crystallization process of an adenosine receptor antagonist using minimal amounts of the sample within a shortened timeframe. Additionally, it provides a scientific rationale for ensuring the selection of the most stable form in the early stages of drug discovery, thereby reducing risks in future pharmaceutical development.

Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes

Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure re-optimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.

Unprecedented Packing Polymorphism of Oxindole: An Exploration Inspired by Crystal Structure Prediction

Crystal polymorphism, characterized by different packing arrangements of the same compound, strongly ties to the physical properties of a molecule. Determining the polymorphic landscape is complex and time-consuming, with the number of experimentally observed polymorphs varying widely from molecule to molecule. Furthermore, disappearing polymorphs, the phenomenon whereby experimentally observed forms cannot be reproduced, pose a significant challenge for the pharmaceutical industry. Herein, we focused on oxindole (OX), a small rigid molecule with four known polymorphs, including a reported disappearing form. Using crystal structure prediction (CSP), we assessed OX solid-state landscape and thermodynamic stability by comparing predicted structures with experimentally known forms. We then performed melt and solution crystallization in bulk and nanoconfinement to validate our predictions. These experiments successfully reproduced the known forms and led to the discovery of four novel polymorphs. Our approach provided insights into reconstructing disappearing polymorphs and building more comprehensive polymorph landscapes. These results also establish a new record of packing polymorphism for rigid molecules.

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.

Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.

Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry

Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality, encompassing crystal growth, sample preparation, and automated data capture. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low-energy structures, their high computational cost is problematic because of the need to evaluate tens to hundreds of thousands of trial crystal structures to fully explore typical crystal energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as the first stage of a hierarchical scheme involving multiple stages of increasingly accurate energy calculations. Machine-learned interatomic potentials (MLIPs), trained to reproduce the results of ab initio methods with computational costs close to those of force fields, can improve the efficiency of the CSP by reducing or eliminating the need for costly DFT calculations. Here, we investigate active learning methods for training MLIPs with CSP datasets. The combination of active learning with the well-developed sampling methods from CSP yields potentials in a highly automated workflow that are relevant over a wide range of the crystal packing space. To demonstrate these potentials, we illustrate efficiently reranking large, diverse crystal structure landscapes to near-DFT accuracy from force field-based CSP, improving the reliability of the final energy ranking. Furthermore, we demonstrate how these potentials can be extended to more accurately model structures far from lattice energy minima through additional on-the-fly training within Monte Carlo simulations.

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.

Illuminating milling mechanochemistry by tandem real-time fluorescence emission and Raman spectroscopy monitoring

In pursuit of accessible and interpretable methods for direct and real-time observation of mechanochemical reactions, we demonstrate a tandem spectroscopic method for monitoring of ball-milling transformations combining fluorescence emission and Raman spectroscopy, accompanied by high-level molecular and periodic density-functional theory (DFT) calculations, including periodic time-dependent (TD-DFT) modelling of solid-state fluorescence spectra. This proof-of-principle report presents this readily accessible dual-spectroscopy technique as capable of observing changes to the supramolecular structure of the model pharmaceutical system indometacin during mechanochemical polymorph transformation and cocrystallisation. The observed time-resolved in situ spectroscopic and kinetic data are supported by ex situ X-ray diffraction and solid-state nuclear magnetic resonance spectroscopy measurements. The application of first principles (ab initio) calculations enabled the elucidation of how changes in crystalline environment, that result from mechanochemical reactions, affect vibrational and electronic excited states of molecules. The herein explored interpretation of both real-time and ex situ spectroscopic data through ab initio calculations provides an entry into developing a detailed mechanistic understanding of mechanochemical milling processes and highlights the challenges of using real-time spectroscopy.

Predicting crystal form stability under real-world conditions

The physicochemical properties of molecular crystals, such as solubility, stability, compactability, melting behaviour and bioavailability, depend on their crystal form1. In silico crystal form selection has recently come much closer to realization because of the development of accurate and affordable free-energy calculations2-4. Here we redefine the state of the art, primarily by improving the accuracy of free-energy calculations, constructing a reliable experimental benchmark for solid-solid free-energy differences, quantifying statistical errors for the computed free energies and placing both hydrate crystal structures of different stoichiometries and anhydrate crystal structures on the same energy landscape, with defined error bars, as a function of temperature and relative humidity. The calculated free energies have standard errors of 1-2 kJ mol-1 for industrially relevant compounds, and the method to place crystal structures with different hydrate stoichiometries on the same energy landscape can be extended to other multi-component systems, including solvates. These contributions reduce the gap between the needs of the experimentalist and the capabilities of modern computational tools, transforming crystal structure prediction into a more reliable and actionable procedure that can be used in combination with experimental evidence to direct crystal form selection and establish control5.

Pushing Technique Boundaries to Probe Conformational Polymorphism

We present an extensive exploration of the solid-form landscape of chlorpropamide (CPA) using a combined experimental-computational approach at the frontiers of both fields. We have obtained new conformational polymorphs of CPA, placing them into context with known forms using flexible-molecule crystal structure prediction. We highlight the formation of a new polymorph (ζ-CPA) via spray-drying experiments despite its notable metastability (14 kJ/mol) relative to the thermodynamic α-form, and we identify and resolve the ball-milled η-form isolated in 2019. Additionally, we employ impurity- and gel-assisted crystallization to control polymorphism and the formation of novel multicomponent forms. We, thus, demonstrate the power of this collaborative screening approach to observe, rationalize, and control the formation of new metastable forms.

Quantitative matching of crystal structures to experimental powder diffractograms

The identification and classification of crystal structures is fundamental in materials science, as the crystal structure is an inherent factor of what gives solid materials their properties. Being able to identify the same crystallographic form from unique origins (e.g. different temperatures, pressures, or in silico-generated) is a complex challenge. While our previous work has focused on comparison of simulated powder diffractograms from known crystal structures, herein is presented the variable-cell experimental powder difference (VC-xPWDF) method to match collected powder diffractograms of unknown polymorphs to both experimental crystal structures from the Cambridge Structural Database and in silico-generated structures from the Control and Prediction of the Organic Solid State database. The VC-xPWDF method is shown to correctly identify the most similar crystal structure to both moderate and "low" quality experimental powder diffractograms for a set of 7 representative organic compounds. Features of the powder diffractograms that are more challenging for the VC-xPWDF method are discussed (i.e. preferred orientation), and comparison with the FIDEL method showcases the advantage of VC-xPWDF provided the experimental powder diffractogram can be indexed. The VC-xPWDF method should allow rapid identification of new polymorphs from solid-form screening studies, without requiring single-crystal analysis.

Vapor Sorption and Halogen-Bond-Induced Solid-Form Rearrangement of a Porous Pharmaceutical

A porous, nonsolvated polymorph of the voltage-gated sodium channel blocker mexiletine hydrochloride absorbs iodine vapor to give a pharmaceutical cocrystal incorporating an I₂Cl^- anion that forms a halogen-π interaction with the mexiletine cations. The most thermodynamically stable form of the compound does not absorb iodine. This example shows that vapor sorption is a potentially useful and underused tool for bringing about changes in pharmaceutical solid form as part of a solid form screening protocol.

Experimentally Validated Ab Initio Crystal Structure Prediction of Novel Metal-Organic Framework Materials

First-principles crystal structure prediction (CSP) is the most powerful approach for materials discovery, enabling the prediction and evaluation of properties of new solid phases based only on a diagram of their underlying components. Here, we present the first CSP-based discovery of metal-organic frameworks (MOFs), offering a broader alternative to conventional techniques, which rely on geometry, intuition, and experimental screening. Phase landscapes were calculated for three systems involving flexible Cu(II) nodes, which could adopt a potentially limitless number of network topologies and are not amenable to conventional MOF design. The CSP procedure was validated experimentally through the synthesis of materials whose structures perfectly matched those found among the lowest-energy calculated structures and whose relevant properties, such as combustion energies, could immediately be evaluated from CSP-derived structures.

Hydrogen-bond organized 2D metal-organic microsheets: direct ultralong phosphorescence and color-tunable optical waveguides

Ultralong phosphorescent materials have numerous applications across biological imaging, light-emitting devices, X-ray detection and anti-counterfeiting. Triplet-state molecular phosphorescence typically accompanies the singlet-state fluorescence during photoluminescence, and it is still difficult to achieve direct triplet photoemission as ultralong room temperature phosphorescence (RTP). Here, we have designed Zn-IMDC (IMDC, 4,5-imidazoledicarboxylic acid) and Cd-IMDC, two-dimensional (2D) hydrogen-bond organized metal-organic crystalline microsheets that exhibit rarely direct ultralong RTP upon UV excitation, benefiting from the appropriate heavy-atom effect and multiple triplet energy levels. The excitation-dependent and thermally stimulated ultralong phosphorescence endow the metal-organic systems great opportunities for information safety application and temperature-gated afterglow emission. The well-defined 2D microsheets present color-tunable and anisotropic optical waveguides under different excitation and temperature conditions, providing an effective way to obtain intelligent RTP-based photonic systems at the micro- and nano-scales.

From Powders to Single Crystals: A Crystallographer's Toolbox for Small-Molecule Structure Determination

Although the crystal structures of small-molecule compounds are often determined from single-crystal X-ray diffraction (scXRD), recent advances in three-dimensional electron diffraction (3DED) and crystal structure prediction (CSP) methods promise to expand the structure elucidation toolbox available to the crystallographer. Herein, a comparative assessment of scXRD, 3DED, and CSP in combination with powder X-ray diffraction is carried out on two former drug candidate compounds and a multicomponent crystal of a key building block in the synthesis of gefapixant citrate.

Chains or rings? Polymorphism of an isoniazid derivative derivatized with diacetone alcohol

Isoniazid was derivated with diacetone alcohol in a Schiff-base reaction in order to yield N'-[(2E)-4-hydroxy-4-methylpentan-2-ylidene]pyridine-4-carbohydrazide. The resulting product was determined to be polymorphic, exhibiting two crystal forms: form I and form II. From the crystal structure determination using SC-XRD it was determined that form I crystalizes in the C2/c space group while form II crystalizes in the P21/c space group. The hydrogen bonding patterns of both forms are distinctively different from each other: form I forms a chain hydrogen bond motif by forming a hydrogen bond between the hydroxyl group and the oxygen of the amide group while form II forms dimers with a ring hydrogen bond motif forming between the hydroxyl group and the pyridine group. From DSC analysis form I and form II are enantiotropically related, with form I converting to form II at 132.3 °C before melting at 142.3 °C. Based on both experimental and computational evidence, we conclude that form I is a metastable form, with form II being the most stable form. This is another case of a "disappearing polymorph."

A density-functional benchmark of vibrational free-energy corrections for molecular crystal polymorphism

Many crystal structure prediction protocols only concern themselves with the electronic energy of molecular crystals. However, vibrational contributions to the free energy ( Fvib) can be significant in determining accurate stability rankings for crystal candidates. While force-field studies have been conducted to gauge the magnitude of these free-energy corrections, highly accurate results from quantum mechanical methods, such as density-functional theory (DFT), are desirable. Here, we introduce the PV17 set of 17 polymorphic pairs of organic molecular crystals, for which plane wave DFT is used to calculate the vibrational free energies and free-energy differences (Δ Fvib) between each pair. Our DFT results confirm that the vibrational free-energy corrections are small, having a mean value of 1.0 kJ/mol and a maximum value of 2.3 kJ/mol for the PV17 set. Furthermore, we assess the accuracy of a series of lower-cost DFT, semi-empirical, and force-field models for computing Δ Fvib that have been proposed in the literature. It is found that calculating Fvib using the Γ-point frequencies does not provide Δ Fvib values of sufficiently high quality. In addition, Δ Fvib values calculated using various approximate methods have mean absolute errors relative to our converged DFT results of equivalent or larger magnitude than the vibrational free-energy corrections themselves. Thus, we conclude that, in a crystal structure prediction protocol, it is preferable to forego the inclusion of vibrational free-energy corrections than to estimate them with any of the approximate methods considered here.

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...

OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors

Materials design and discovery are often hampered by the slow pace and materials and human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, and data science techniques, however, are being manifest in a convergence of these tools to enable in silico materials discovery. Here, we present the development and deployment of computational materials data and data analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals in Electronic and Light-Oriented Technologies) infrastructure, consisting of a Python-based OCELOT application programming interface and OCELOT database, is designed to enable rapid materials exploration. The database contains a descriptor-based schema for high-throughput calculations that have been implemented on more than 56 000 experimental crystal structures derived from 47 000 distinct molecular structures. OCELOT is open-access and accessible via a web-user interface at https://oscar.as.uky.edu.

The unexpected discovery of the ninth polymorph of tolfenamic acid

A new polymorph of tolfenamic acid, form IX, has been crystallised from a simple cooling crystallisation experiment raising the question as to why this polymorph had never been reported before.

Exploring the solid form landscape of the antifungal drug isavuconazole: crystal structure analysis, phase transformation behavior and dissolution performance

Phase transformation of ISV solid forms during dissolution.

Rich polymorphism in nicotinamide revealed by melt crystallization and crystal structure prediction

Overprediction is a major limitation of current crystal structure prediction (CSP) methods. It is difficult to determine whether computer-predicted polymorphic structures are artefacts of the calculation model or are polymorphs that have not yet been found. Here, we reported the well-known vitamin nicotinamide (NIC) to be a highly polymorphic compound with nine solved single-crystal structures determined by performing melt crystallization. A CSP calculation successfully identifies all six Z' = 1 and 2 experimental structures, five of which defy 66 years of attempts at being explored using solution crystallization. Our study demonstrates that when combined with our strategy for cultivating single crystals from melt microdroplets, melt crystallization has turned out to be an efficient tool for exploring polymorphic landscapes to better understand polymorphic crystallization and to more effectively test the accuracy of theoretical predictions, especially in regions inaccessible by solution crystallization.