Hydrogen-bond coordination in organic crystal structures: statistics, predictions and applications

Galek, Peter T. A.; Chisholm, James A.; Pidcock, Elna; Wood, Peter A.

doi:10.1107/s2052520613033003

Cited by 45 publications

(48 citation statements)

References 69 publications

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“…In attempting to provide a greater definition of the probability of a new form appearing tools ranging from quantum chemistry analysis, hydrogen bonding statistics and full polymorph prediction can be applied either indi-vidually or in combination. Three groups of tools worth highlighting are: (1) sigma charges from quantum chemistry; [48] (2) hydrogen bond propensity and other solid-form informatics tools -the focus of this paper; [25][26][27][28][29]35] (3) crystal structure (polymorph) prediction described elsewhere. [49] Recent developments in quantum chemistry provide an integrated approach that combines the aspects of continuum solvation and surface interaction.…”

Section: Discussionmentioning

confidence: 99%

“…The intermolecular interactions for crizotinib were assessed using the hydrogen bond propensity tool. [25][26][27][28] Using donor-acceptor contact distances of van der Waals radii + 0.1 Å, a Donor-H···Acceptor angle of ≥120°and the functional groups as defined in Figure 9 (top), the structure was analysed using a statistical model built from a training data set of 1429 structures. The results from the hydrogen bond propensity calculation are shown in Table 1 and Figure 10.…”

Section: The Solid Form Of Crizotinibmentioning

confidence: 99%

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The integration of solid-form informatics into solid-form selection

Feeder

Pidcock

Reilly

et al. 2015

Journal of Pharmacy and Pharmacology

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Section: Discussionmentioning

confidence: 99%

Section: The Solid Form Of Crizotinibmentioning

confidence: 99%

The integration of solid-form informatics into solid-form selection

Feeder

Pidcock

Reilly

et al. 2015

Journal of Pharmacy and Pharmacology

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“…[186]. Two X-rayed polymorphs of 4-aminobenzoic acid realize different H-bonding, and the more stable form realizes more likely sets of H-bonding [187]. The approach was also successfully used to rationalize polymorphism for N2-(indol-3-acetyl)-L-aspargin [186], lamotrigine [188], and crizotinib [189,190] (see Figure 8 for chemical formulas of these compounds).…”

Section: Polymorph Assessmentmentioning

confidence: 99%

“…Thus, although this method is not able to predict the ratio of components in a co-crystal, it can be applied for co- Known forms of paracetamol both exhibit H-bonding between OH groups, that is a less likely donor to form a bond than an amide fragment. It was assumed that there was a conflict between a good donor group to form a bond disfavoring a poor acceptor to form bonding, which could be overcome by co-crystal formation with molecules containing the same D and A groups [187]. Indeed, co-crystals of paracetamol with diamino-or bis(4-pyridyl)-containing co-formers contain molecules of paracetamol connected by more likely amide/hydroxo or amide/amide pairs of interactions [193].…”

Section: Polymorph Assessmentmentioning

confidence: 99%

Intermolecular Interactions in Functional Crystalline Materials: From Data to Knowledge

Вологжанина

2019

Crystals

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Intermolecular interactions of organic, inorganic, and organometallic compounds are the key to many composition-structure and structure-property networks. In this review, some of these relations and the tools developed by the Cambridge Crystallographic Data Center (CCDC) to analyze them and design solid forms with desired properties are described. The potential of studies supported by the Cambridge Structural Database (CSD)-Materials tools for investigation of dynamic processes in crystals, for analysis of biologically active, high energy, optical, (electro)conductive, and other functional crystalline materials, and for the prediction of novel solid forms (polymorphs, co-crystals, solvates) are discussed. Besides, some unusual applications, the potential for further development and limitations of the CCDC software are reported. Crystals 2019, 9, 478 2 of 40 these fields, the manuscript cites only (i) recent works devoted to the analysis of correlations between intermolecular interactions and properties of a small molecule (for example its inclination to form polymorphs, solvates, and co-crystals), or a corresponding solid (from a well-known requirement for non-linear optical materials to crystallize in acentric space groups, to recent studies devoted to the effect of solvent presence on mechanical properties), and (ii) the corresponding software developed to investigate these correlations and to design novel solid forms with desired physicochemical properties. As the description of structure-property networks describes mainly the papers published in the last 10 years in the field of organic, organometallic, and coordination crystals, then the software under discussion will be limited with those developed by the Cambridge Crystallographic Data Centre (CCDC) for material chemistry and crystallography. Various examples of applications of the CCDC software to functional materials including their combination with other software, and restrictions found, will be given.First, the Cambridge Structural Database (CSD) and components of the CSD-Enterprise will be described in Section 2. Then, some properties related to the appearance of a given supramolecular associate, and the tools to search for an associate in the CSD will be reported in Section 3. The properties of solids dependent on the crystal morphology, the Bravais, Friedel, Donnay, and Harker (BFDH) tool for crystal morphology prediction and its' application to affect crystal morphology, polymorphism, and solvatomorphism will be reported in Section 4. Knowledge-based predictions of H-bonded polymorphism, co-crystal formation, mapping of likely intermolecular interactions, and conformer generation for the synthesis of novel functional materials will be discussed in Section 5, and the analysis of local connectivity and whole architectures of solvent molecules in Section 6. Finally, Section 7 contains information about Python API algorithms compatible with the CSD-Enterprise and about some examples of the successful combination of the CSD-Enterprise tools with exte...

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“…van de Streek and Motherwell noted that "statistical surveys into the behaviour of hydrates are difficult due to the severe bias that is introduced at many levels 8 ", however there may be scope within similar surveying techniques for the building of predictive models. For example, Galek et al 12 have utilised data available in the CSD to develop statistical models for hydrogen-bond coordination behaviour (not limited to the study of hydrates). Their work describes the hydrogen bonding behaviour of over 70 unique atom types, and begins to make assessments of structural stability of hydrogen bonding environments in known crystal structures, showing potential for application of empirically or statistically derived models.…”

Section: Introductionmentioning

confidence: 99%

Probing the average distribution of water in organic hydrate crystal structures with radial distribution functions (RDFs)

2017

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The abundance of crystal structures of solvated organic molecules reflects the common role of solvent in the crystallisation process. An understanding of solvation is therefore important for crystal engineering, with solvent choice often affecting polymorphism as well as influencing the crystal structure. Of particular importance is the role of water, and a number of approaches have previously been considered in the analysis of large datasets of organic hydrates. In this work we attempt to develop a method suitable for application to organic hydrate crystal structures, in order to better understand the distribution of water molecules in such systems. We present a model aimed at combining the distribution functions of multiple atom pairs from a number of crystal structures. From this, we can comment qualitatively on the average distribution of water in organic hydrates.UK † Electronic supplementary information (ESI) available: All MATLAB scripts used to calculate the RDFs described, and the resulting RDFs, a list of refcodes for the crystal structures used, and a list of their measurement temperatures. See

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Hydrogen-bond coordination in organic crystal structures: statistics, predictions and applications

Cited by 45 publications

References 69 publications

The integration of solid-form informatics into solid-form selection

The integration of solid-form informatics into solid-form selection

Intermolecular Interactions in Functional Crystalline Materials: From Data to Knowledge

Probing the average distribution of water in organic hydrate crystal structures with radial distribution functions (RDFs)

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