Co-crystal Prediction by Artificial Neural Networks*

Crystal Structure Landscape of Diarylethene-Based Crystalline Solids: A Comprehensive CSD Analysis

Diarylethenes (DAEs) are an exciting class of stimulus-responsive organic molecules that exhibit electrocyclization reactions upon exposure to light, heat, or other stimuli. The rational design of DAE-based crystalline materials is, however, complicated by the presence of DAE atropisomers, only one of which is photoactive. Data mining of the CSD produced 1349 unique molecular DAE structures that were subsequently analyzed according to selected chemical and geometric attributes. Additional analyses were performed on 1078 dithienylethene (DTE) structures-the largest subgroup within the ensemble. The crystal structure landscape, based upon D-D parameterization and analysis, revealed a vast array of molecular geometries, many of which may not correspond to energetic minima. The analyses link various chemical and geometric parameters to isomers observed in the lattice and their reactivity; however, potential biases intrinsic to this ensemble of structures complicate the determination of causal relationships. We believe that this retrospective comprehensive analysis of DAE structures represents an important step for understanding more broadly the crystal landscape of this class of materials.

Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4'-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.

Optimizing Drug Development: Harnessing the Sustainability of Pharmaceutical Cocrystals

Environmental impacts of the industrial revolution necessitate adoption of sustainable practices in all areas of development. The pharmaceutical industry faces increasing pressure to minimize its ecological footprint due to its significant contribution to environmental pollution. Over the past two decades, pharmaceutical cocrystals have received immense popularity due to their ability to optimize the critical attributes of active pharmaceutical ingredients and presented an avenue to bring improved drug products to the market. This review explores the potential of pharmaceutical cocrystals as an ecofriendly alternative to traditional solid forms, offering a sustainable approach to drug development. From reducing the number of required doses to improving the stability of actives, from eliminating synthetic operations to using pharmaceutically approved chemicals, from the use of continuous and solvent-free manufacturing methods to leveraging published data on the safety and toxicology, the cocrystallization approach contributes to sustainability of drug development. The latest trends suggest a promising role of pharmaceutical cocrystals in bringing novel and improved medicines to the market, which has been further fuelled by the recent guidance from the major regulatory agencies.

Achieving High-Temperature Phosphorescence by Organic Cocrystal Engineering

Organic phosphors offer a promising alternative in optoelectronics, but their temperature-sensitive feature has restricted their applications in high-temperature scenarios, and the attainment of high-temperature phosphorescence (HTP) is still challenging. Herein, a series of organic cocrystal phosphors are constructed by supramolecular assembly with an ultralong emission lifetime of up to 2.16 s. Intriguingly, remarkable stabilization of triplet excitons can also be realized at elevated temperature, and green phosphorescence is still exhibited in solid state even up to 150 °C. From special molecular packing within the crystal lattice, it has been observed that the orientation of isolated water cluster and well-controlled molecular organization via multiple interactions can favor the structural rigidity of cocrystals more effectively to suppress the nonradiative transition, thus resulting in efficient room-temperature phosphorescence and unprecedented survival of HTP.

Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties

Cocrystal engineering, which involves the self-assembly of two or more components into a solid-state supramolecular structure through non-covalent interactions, has emerged as a promising approach to tailor the physicochemical properties of active pharmaceutical ingredient (API). Efficient coformer screening for cocrystal remains a challenge. Herein, a prediction strategy based on machine learning algorithms was employed to predict cocrystal formation and seven reliable models with accuracy over 0.890 were successfully constructed. Imatinib was selected as the model drug and the models established were applied to screen 31 potential coformers. Experimental verification results indicated RF-8 is the optimal model among seven models with an accuracy of 0.839. When the seven models were combined for coformer screening of Imatinib, the combinational model achieved an accuracy of 0.903, and eight new solid forms were observed and characterized. Benefiting from intermolecular interactions, the obtained multicomponent crystals displayed enhanced physicochemical properties. Dissolution and solubility experiments showed the prepared multicomponent crystals had higher cumulative dissolution rate and remarkably improved the solubility of imatinib, and IM-MC exhibited comparable solubility to Imatinib mesylate α form. Stability test and cytotoxicity results showed that multicomponent crystals exhibited excellent stability and the drug-drug cocrystal IM-5F exhibited higher cytotoxicity than pure API.

Thermodynamic Stability Is a Poor Indicator of Cocrystallization in Models of Organic Molecules

Cocrystallizing a given molecule with another can be useful for adjusting the physical properties of molecules in the solid state. However, most combinations of molecules do not readily cocrystallize but form either one-component crystals or amorphous solids. Computational methods of crystal structure prediction can, in principle, identify the thermodynamically stable cocrystal and thus predict if molecules will cocrystallize or not. However, the pronounced polymorphism and tendency of many organic molecules to form disordered solids suggest that kinetic factors can play an important role in cocrystallization. The question remains: if a binary system of molecules has a thermodynamically stable cocrystal, will it indeed cocrystallize? To address this question, we simulate the crystallization of more than 2600 distinct pairs of chiral model molecules of similar size in 2D and calculate accurate crystal energy landscapes for all of them. Our analysis shows that thermodynamic criteria alone are unreliable in the prediction of cocrystallization. While the vast majority of cocrystals that form in our simulations are thermodynamically favorable, most coformer systems that have a thermodynamically stable cocrystal do not cocrystallize. We furthermore show that cocrystallization rates increase 3-fold when coformers are used that do not form well-ordered single-component crystals. Our results suggest that kinetic factors of cocrystallization are decisive in many cases.

MolNet_Equi: A Chemically Intuitive, Rotation-Equivariant Graph Neural Network

Although deep-learning (DL) models suggest unprecedented prediction capabilities in tackling various chemical problems, their demonstrated tasks have so far been limited to the scalar properties including the magnitude of vectorial properties, such as molecular dipole moments. A rotation-equivariant MolNet_Equi model, proposed in this paper, understands and recognizes the molecular rotation in the 3D Euclidean space, and exhibits the ability to predict directional dipole moments in the rotation-sensitive mode, as well as showing superior performance for the prediction of scalar properties. Three consecutive operations of molecular rotationR M ${\left(R\left(M\right)\right)}$ , dipole-moment predictionφ μ R M ${\left({\phi{} }_{\mu }\left(R\left(M\right)\right)\right)}$ , and dipole-moment inverse-rotationR - 1 φ μ R M ${\left({R}^{-1}\left({\phi{} }_{\mu }\left(R\left(M\right)\right)\right)\right)}$ do not alter the original prediction of the total dipole moment of a moleculeφ μ M ${\left({\phi{} }_{\mu }\right(M\left)\right)}$ , assuring the rotational equivariance of MolNet_Equi. Furthermore, MolNet_Equi faithfully predicts the absolute direction of dipole moments given molecular poses, albeit the model has been trained only with the information on dipole-moment magnitudes, not directions. This work highlights the potential of incorporating fundamental yet crucial chemical rules and concepts into DL models, leading to the development of chemically intuitive models.

In silico co-crystal design: Assessment of the latest advances

Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active pharmaceutical ingredient and can lead to improvements in physicochemical properties of clinical relevance. At the same time, machine learning is finding its way into all areas of drug discovery and delivers impressive results. In this review, we attempt to provide an overview of machine learning, deep learning and network-based recommendation approaches applied to pharmaceutical co-crystallization. We also present crystal structure prediction as an alternative to machine learning approaches.

Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs

Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-amorphous systems, are a result of the combination of an active pharmaceutical ingredient (API) with a pharmaceutically acceptable co-former. These solid forms can enhance the physicochemical and pharmacokinetic properties of APIs, making them increasingly interesting and important in recent decades. Nevertheless, predicting the formation of API multicomponent solid forms in the early stages of formulation development can be challenging, as it often requires significant time and resources. To address this, empirical and computational methods have been developed to help screen for potential co-formers more efficiently and accurately, thus reducing the number of laboratory experiments needed. This review provides a comprehensive overview of current screening and prediction methods for the formation of API multicomponent solid forms, covering both crystalline states (co-crystals and salts) and amorphous forms (co-amorphous). Furthermore, it discusses recent advances and emerging trends in prediction methods, with a particular focus on artificial intelligence.

Pharmaceutical Cocrystal Discovery via 3D-SMINBR: A New Network Recommendation Tool Augmented by 3D Molecular Conformations

Cocrystals have significant potential in various fields such as chemistry, material, and medicine. For instance, pharmaceutical cocrystals have the ability to address issues associated with physicochemical and biopharmaceutical properties. However, it can be challenging to find proper coformers to form cocrystals with drugs of interest. Herein, a new in silico tool called 3D substructure-molecular-interaction network-based recommendation (3D-SMINBR) has been developed to address this problem. This tool first integrated 3D molecular conformations with a weighted network-based recommendation model to prioritize potential coformers for target drugs. In cross-validation, the performance of 3D-SMINBR surpassed the 2D substructure-based predictive model SMINBR in our previous study. Additionally, the generalization capability of 3D-SMINBR was confirmed by testing on unseen cocrystal data. The practicality of this tool was further demonstrated by case studies on cocrystal screening of armillarisin A (Arm) and isoimperatorin (iIM). The obtained Arm-piperazine and iIM-salicylamide cocrystals present improved solubility and dissolution rate compared to their parent drugs. Overall, 3D-SMINBR augmented by 3D molecular conformations would be a useful network-based tool for cocrystal discovery. A free web server for 3D-SMINBR can be freely accessed at http://lmmd.ecust.edu.cn/netcorecsys/.

Overcoming the Hydrophobic Nature of Zinc Phenylacetate Through Co-Crystallization with Isonicotinamide

Zinc phenylacetate (Zn-PA), a substitute for sodium phenylacetate as an ammonia-scavenging drug is hydrophobic, which poses problems for drug dissolution and solubility. We were able to co-crystallize the zinc phenylacetate with isonicotinamide (INAM) and produce a novel crystalline compound (Zn-PA-INAM). The single crystal of this new crystal was obtained, and its structure is reported here for the first time. Zn-PA-INAM was characterized computationally by ab initio, Hirshfeld calculations, CLP-PIXEL lattice energy calculation, and BFDH morphology analysis, and experimentally by PXRD, Sc-XRD, FTIR, DSC, and TGA analyses. Structural and vibrational analyses showed a major modification in intermolecular interaction of Zn-PA-INAM compared to Zn-PA. The dispersion-based pi-stacking in Zn-PA is replaced by coulomb-polarization effect of hydrogen bonds. As a result, Zn-PA-INAM is hydrophilic, improving the wettability and powder dissolution of the target compound in an aqueous solution. Morphology analysis revealed, unlike Zn-PA, Zn-PA-INAM has polar groups exposed on its prominent crystalline faces, reducing the hydrophobicity of the crystal. The shift in average water droplet contact angle from 128.1° (Zn-PA) to 27.1° (Zn-PA-INAM) is strong evidence of a marked decrease in hydrophobicity of the target compound. Finally, HPLC was used to obtain the dissolution profile and solubility of Zn-PA-INAM compared to Zn-PA.

Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation

The application of computational screening methodologies based on H-bond propensity scores, molecular complementarity, molecular electrostatic potentials, and crystal structure prediction has guided the discovery of novel cocrystals of dapsone and bipyridine (DDS:BIPY). The experimental screen, which included mechanochemical and slurry experiments as well as the contact preparation, resulted in four cocrystals, including the previously known DDS:4,4'-BIPY (2:1, CC₄₄-B) cocrystal. To understand the factors governing the formation of the DDS:2,2'-BIPY polymorphs (1:1, CC₂₂-A and CC₂₂-B) and the two DDS:4,4'-BIPY cocrystal stoichiometries (1:1 and 2:1), different experimental conditions (such as the influence of solvent, grinding/stirring time, etc.) were tested and compared with the virtual screening results. The computationally generated (1:1) crystal energy landscapes had the experimental cocrystals as the lowest energy structures, although distinct cocrystal packings were observed for the similar coformers. H-bonding scores and molecular electrostatic potential maps correctly indicated cocrystallization of DDS and the BIPY isomers, with a higher likelihood for 4,4'-BIPY. The molecular conformation influenced the molecular complementarity results, predicting no cocrystallization for 2,2'-BIPY with DDS. The crystal structures of CC₂₂-A and CC₄₄-A were solved from powder X-ray diffraction data. All four cocrystals were fully characterized by a range of analytical techniques, including powder X-ray diffraction, infrared spectroscopy, hot-stage microscopy, thermogravimetric analysis, and differential scanning calorimetry. The two DDS:2,2'-BIPY polymorphs are enantiotropically related, with form B being the stable polymorph at room temperature (RT) and form A being the higher temperature form. Form B is metastable but kinetically stable at RT. The two DDS:4,4'-BIPY cocrystals are stable at room conditions; however, at higher temperatures, CC₄₄-A transforms to CC₄₄-B. The cocrystal formation enthalpy order, derived from the lattice energies, was calculated as follows: CC₄₄-B > CC₄₄-A > CC₂₂-A.

The Benefits and Challenges of Antibiotics-Non-Steroidal Anti-Inflammatory Drugs Non-Covalent Reaction

Recently, non-covalent reactions have emerged as approaches to improve the physicochemical properties of active pharmaceutical ingredients (API), including antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs). This review aimed to present and discuss the non-covalent reaction products of antibiotics, including salt and neutral multi-component solid forms, by framing their substituents and molar ratios, manufacturing techniques, characterization methods, benefits, potency changes, and toxicity, and is completed with an analysis of the development of computational models used in this field. Based on the data, NSAIDs are the most-developed drugs in multi-component system preparations, followed by antibiotics, i.e., antituberculosis and fluoroquinolones. They have reacted with inorganic elements, excipients, nutraceuticals, natural products, and other drugs. However, in terms of treatments for common infections, fluoroquinolones are more frequently used. Generally, NSAIDs are acquired on an over-the-counter basis, causing inappropriate medication. In addition, the pKa differences between the two groups of medicine offer the potential for them to react non-covalently. Hence, this review highlights fluoroquinolone-NSAID multi-component solid systems, which offer some benefits. These systems can increase patient compliance and promote the appropriate monitoring of drug usage; the dual drug multi-component solids have been proven to improve the physicochemical properties of one or both components, especially in terms of solubility and stability. In addition, some reports show an enhancement of the antibiotic activity of the products. However, it is important to consider the possibility of activity changes, interaction, and toxicity when using drug combinations. Hence, these aspects also are discussed in this review. Finally, we present computational modeling, which has been utilized broadly to support multi-component system designs, including coformer screening, preparation methods, and structural modeling, as well as to predict physicochemical properties, potency, and toxicity. This integrated review is expected to be useful for further antibiotic-NSAID multi-component system development.

Absolute Configuration Determination of Chiral API Molecules by MicroED Analysis of Cocrystal Powders Formed Based on Cocrystal Propensity Prediction Calculations

Establishing the absolute configuration of chiral active pharmaceutical ingredients (APIs) is of great importance. Single crystal X-ray diffraction (scXRD) has traditionally been the method of choice for such analysis, but scXRD requires the growth of large crystals, which can be challenging. Here, we present a method for determining absolute configuration that does not rely on the growth of large crystals. By examining microcrystals formed with chiral probes (small chiral compounds such as amino acids), absolute configuration can be unambiguously determined by microcrystal electron diffraction (MicroED). Our streamlined method employs three steps: (1) virtual screening to identify promising chiral probes, (2) experimental cocrystal screening and (3) structure determination by MicroED and absolute configuration assignment. We successfully applied this method to analyze two chiral API molecules currently on the market for which scXRD was not used to determine absolute configuration.

Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals

In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through the successful combination of the drug with the positional isomers of acetamidobenzoic acid. The structural and energetic features of the CBZ cocrystals with 3- and 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. The ability of three fundamentally different virtual screening methods to predict the correct cocrystallization outcome for CBZ was assessed based on the new experimental results obtained in this study and data available in the literature. It was found that the hydrogen bond propensity model performed the worst in distinguishing positive and negative results of CBZ cocrystallization experiments with 87 coformers, attaining an accuracy value lower than random guessing. The method that utilizes molecular electrostatic potential maps and the machine learning approach named CCGNet exhibited comparable results in terms of prediction metrics, albeit the latter resulted in superior specificity and overall accuracy while requiring no time-consuming DFT computations. In addition, formation thermodynamic parameters for the newly obtained CBZ cocrystals with 3- and 4-acetamidobenzoic acids were evaluated using temperature dependences of the cocrystallization Gibbs energy. The cocrystallization reactions between CBZ and the selected coformers were found to be enthalpy-driven, with entropy terms being statistically different from zero. The observed difference in dissolution behavior of the cocrystals in aqueous media was thought to be caused by variations in their thermodynamic stability.

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation

Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph.

Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry

The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties of poorly water-soluble drug compounds. However, the successful discovery of new pharmaceutical cocrystals remains a time- and resource-consuming process. This study proposes the use of a combined computational-experimental high-throughput approach as a tool to accelerate and improve the efficiency of cocrystal screening exemplified by posaconazole. First, we employed the COSMOquick software to preselect and rank cocrystal candidates (coformers). Second, high-throughput crystallization experiments (HTCS) were conducted on the selected coformers. The HTCS results were successfully reproduced by liquid-assisted grinding and reaction crystallization, ultimately leading to the synthesis of thirteen new posaconazole cocrystals (7 anhydrous, 5 hydrates, and 1 solvate). The posaconazole cocrystals were characterized by PXRD, ¹H NMR, Fourier transform-Raman, thermogravimetry-Fourier transform infrared spectroscopy, and differential scanning calorimetry. In addition, the prediction performance of COSMOquick was compared to that of two alternative knowledge-based methods: molecular complementarity (MC) and hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC and COSMOquick show good discriminatory ability, suggesting their use as a potential virtual tool to improve cocrystal screening.

Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

Bexarotene (BEX) was approved by the FDA in 1999 for the treatment of cutaneous T-cell lymphoma (CTCL). The poor aqueous solubility causes the low bioavailability of the drug and thereby limits the clinical application. In this study, we developed a GCN-based deep learning model (CocrystalGCN) for in-silico screening of the cocrystals of BEX. The results show that our model obtained high performance relative to baseline models. The top 30 of 109 coformer candidates were scored by CocrystalGCN and then validated experimentally. Finally, cocrystals of BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate were successfully obtained. The crystal structures were determined by single-crystal X-ray diffraction. Powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis were utilized to characterize these multi-component forms. All cocrystals present superior solubility and dissolution over the parent drug. The pharmacokinetic studies show that the plasma exposures (AUC_0−8h) of BEX-pyrazine and BEX-2,5-dimethylpyrazine are 1.7 and 1.8 times that of the commercially available BEX powder, respectively. This work sets a good example for integrating virtual prediction and experimental screening to discover the new cocrystals of water-insoluble drugs.

Comparing and Quantifying the Efficiency of Cocrystal Screening Methods for Praziquantel

Pharmaceutical cocrystals are highly interesting due to their effect on physicochemical properties and their role in separation technologies, particularly for chiral molecules. Detection of new cocrystals is a challenge, and robust screening methods are required. As numerous techniques exist that differ in their crystallization mechanisms, their efficiencies depend on the coformers investigated. The most important parameters characterizing the methods are the (a) screenable coformer fraction, (b) coformer success rate, (c) ability to give several cocrystals per successful coformer, (d) identification of new stable phases, and (e) experimental convenience. Based on these parameters, we compare and quantify the performance of three methods: liquid-assisted grinding, solvent evaporation, and saturation temperature measurements of mixtures. These methods were used to screen 30 molecules, predicted by a network-based link prediction algorithm (described in Cryst. Growth Des. 2021, 21(6), 3428-3437) as potential coformers for the target molecule praziquantel. The solvent evaporation method presented more drawbacks than advantages, liquid-assisted grinding emerged as the most successful and the quickest, while saturation temperature measurements provided equally good results in a slower route yielding additional solubility information relevant for future screenings, single-crystal growth, and cocrystal production processes. Seventeen cocrystals were found, with 14 showing stability and 12 structures resolved.

Nano- and Crystal Engineering Approaches in the Development of Therapeutic Agents for Neoplastic Diseases

Cancer is a leading cause of death worldwide. It is a global quandary that requires the administration of many different active pharmaceutical ingredients (APIs) with different characteristics. As is the case with many APIs, cancer treatments exhibit poor aqueous solubility which can lead to low drug absorption, increased doses, and subsequently poor bioavailability and the occurrence of more adverse events. Several strategies have been envisaged to overcome this drawback, specifically for the treatment of neoplastic diseases. These include crystal engineering, in which new crystal structures are formed to improve drug physicochemical properties, and/or nanoengineering in which the reduction in particle size of the pristine crystal results in much improved physicochemical properties. Co-crystals, which are supramolecular complexes that comprise of an API and a co-crystal former (CCF) held together by non-covalent interactions in crystal lattice, have been developed to improve the performance of some anti-cancer drugs. Similarly, nanosizing through the formation of nanocrystals and, in some cases, the use of both crystal and nanoengineering to obtain nano co-crystals (NCC) have been used to increase the solubility as well as overall performance of many anticancer drugs. The formulation process of both micron and sub-micron crystalline formulations for the treatment of cancers makes use of relatively simple techniques and minimal amounts of excipients aside from stabilizers and co-formers. The flexibility of these crystalline formulations with regards to routes of administration and ability to target neoplastic tissue makes them ideal strategies for effectiveness of cancer treatments. In this review, we describe the use of crystalline formulations for the treatment of various neoplastic diseases. In addition, this review attempts to highlight the gaps in the current translation of these potential treatments into authorized medicines for use in clinical practice.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

The extensive solid-form landscape of sulfathiazole: geometrical similarity and interaction energies

Sulfathiazole shows one of the most extensive solid-form landscapes known to date for an active pharmaceutical ingredient. A standardised structure set of 5 polymorphs, 59 co-crystals, 29 salts, and 3 other structures is established.

A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared

Pelubiprofen (PF), a biopharmaceutical classification system (BCS) class II non-steroidal anti-inflammatory drug, has been on the market only in its crystalline form. To discover the first cocrystal form(s) of the...

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π-π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community.

Size Doesn't Matter: Predicting Physico- or Biochemical Properties Based on Dozens of Molecules

The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. This methodology assumes that a model built on a sufficient amount of data captures general features of the chemical compound structure on which it was trained and that the further reuse of these features on a data set with a lack of data will greatly improve the quality of the new model. In this paper, we develop this approach for small organic molecules, implementing transfer learning with graph convolutional neural networks. The paper shows a significant improvement in the performance of the models for target properties with a lack of data. The effects of the data set composition on the model's quality and the applicability domain of the resulting models are also considered.

SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation

Two-component crystals such as pharmaceutical cocrystals and salts have been proven as an effective strategy to improve physicochemical and biopharmaceutical properties of drugs. It is not easy to select proper molecular combinations to form two-component crystals. The network-based models have been successfully utilized to guide cocrystal design. Yet, the traditional social network-derived methods based on molecular-interaction topology information cannot directly predict interaction partners for new chemical entities (NCEs) that have not been observed to form two-component crystals. Herein, we proposed an effective tool, namely substructure-molecular-interaction network-based recommendation (SMINBR), to prioritize potential interaction partners for NCEs. This in silico tool incorporates network and chemoinformatics methods to bridge the gap between NCEs and known molecular-interaction network. The high performance of 10-fold cross validation and external validation shows the high accuracy and good generalization capability of the model. As a case study, top 10 recommended coformers for apatinib were all experimentally confirmed and a new apatinib cocrystal with paradioxybenzene was obtained. The predictive capability of the model attributes to its accordance with complementary patterns driving the formation of intermolecular interactions. SMINBR could automatically recommend new interaction partners for a target molecule, and would be an effective tool to guide cocrystal design. A free web server for SMINBR is available at http://lmmd.ecust.edu.cn/sminbr/.

Pharmaceutical cocrystals: A review of preparations, physicochemical properties and applications

Pharmaceutical cocrystals are multicomponent systems in which at least one component is an active pharmaceutical ingredient and the others are pharmaceutically acceptable ingredients. Cocrystallization of a drug substance with a coformer is a promising and emerging approach to improve the performance of pharmaceuticals, such as solubility, dissolution profile, pharmacokinetics and stability. This review article presents a comprehensive overview of pharmaceutical cocrystals, including preparation methods, physicochemical properties, and applications. Furthermore, some examples of drug cocrystals are highlighted to illustrate the effect of crystal structures on the various aspects of active pharmaceutical ingredients, such as physical stability, chemical stability, mechanical properties, optical properties, bioavailability, sustained release and therapeutic effect. This review will provide guidance for more efficient design and manufacture of pharmaceutical cocrystals with desired physicochemical properties and applications.

Cocrystals of Praziquantel: Discovery by Network-Based Link Prediction

Cocrystallization has been promoted as an attractive early development tool as it can change the physicochemical properties of a target compound and possibly enable the purification of single enantiomers from racemic compounds. In general, the identification of adequate cocrystallization candidates (or coformers) is troublesome and hampers the exploration of the solid-state landscape. For this reason, several computational tools have been introduced over the last two decades. In this study, cocrystals of Praziquantel (PZQ), an anthelmintic drug used to treat schistosomiasis, are predicted with network-based link prediction and experimentally explored. Single crystals of 12 experimental cocrystal indications were grown and subjected to a structural analysis with single-crystal X-ray diffraction. This case study illustrates the power of the link-prediction approach and its ability to suggest a diverse set of new coformer candidates for a target compound when starting from only a limited number of known cocrystals.

Machine Learning-Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis

Crystallographic facets in a crystal carry interior properties and proffer rich functionalities in a wide range of application areas. However, rational prediction, on-demand customization, and accurate synthesis of facets and facet junctions of a crystal are enormously desirable but still challenging. Herein, a framework of machine learning (ML)-aided crystal facet design with ionic liquid controllable synthesis is developed and then demonstrated with the star-material anatase TiO₂ . Aided by employing ML to acquire surface energies from facet junction datasource, the relationships between surface energy and growth conditions based on the Langmuir adsorption isotherm are unveiled, enabling to develop controllable facet synthetic strategies. These strategies are successfully verified after applied for synthesizing TiO₂ crystals with custom crystal facets and facet junctions under tuning ionic liquid [bmim][BF₄ ] experimental conditions. Therefore, this innovative framework integrates data-intensive rational design and experimental controllable synthesis to develop and customize crystallographic facets and facet junctions. This proves the feasibility of an intelligent chemistry future to accelerate the discovery of facet-governed functional material candidates.

Cocrystal Prediction Using Machine Learning Models and Descriptors

Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.

Supramolecular salts assembled by melamine and two organic hydroxyl acids: synthesis, structure, hydrogen bonds, and luminescent property

Two novel supramolecular salts are synthesized by melamine and organic hydroxyl carboxylic acids. The structure prediction, purity, luminescent property, and thermal stability are investigated by theoretical and experimental analysis.

Co-crystal Prediction by Artificial Neural Networks*

A significant amount of attention has been given to the design and synthesis of co-crystals by both industry and academia because of its potential to change a molecule's physicochemical properties. Yet, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co-crystals, hampering the efficient exploration of the target's solid-state landscape. This paper reports on the application of a data-driven co-crystal prediction method based on two types of artificial neural network models and co-crystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a co-crystal is likely to form. By combining the output of multiple models of both types, our approach shows to have excellent performance on the proposed co-crystal training and validation sets, and has an estimated accuracy of 80 % for molecules for which previous co-crystallization data is unavailable.