<i>COMPACK</i>: a program for identifying crystal structure similarity using distances

Chisholm, James A.; Motherwell, Sam

doi:10.1107/s0021889804027074

Cited by 333 publications

(351 citation statements)

References 9 publications

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“…COMPACK is a program developed by Chisholm et al 35 to compare molecular crystal structures. It is interfaced with the Cambridge Structural Database System through the CSD Python API.…”

Section: Numerical Testsmentioning

confidence: 99%

“…The distance between two configurations can be measured by the Euclidean distance between their BCMs. For molecular crystals, Chisholm et al 35 used intermolecular contact distances and a matching algorithm to characterize and compare structures. Atomic and molecular environment descriptors are also needed in the context of machine learning schemes for force fields, [36][37][38] bonding pattern recognition, 39 or to compare vacancy, interstitial, and intercalation sites.…”

Section: Introductionmentioning

confidence: 99%

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A fingerprint based metric for measuring similarities of crystalline structures

Zhu

Amsler

Fuhrer

et al. 2016

The Journal of Chemical Physics

130

124

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Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings. C 2016 AIP Publishing LLC. [http://dx

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“…COMPACK is a program developed by Chisholm et al 35 to compare molecular crystal structures. It is interfaced with the Cambridge Structural Database System through the CSD Python API.…”

Section: Numerical Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fingerprint based metric for measuring similarities of crystalline structures

Zhu

Amsler

Fuhrer

et al. 2016

The Journal of Chemical Physics

130

124

View full text Add to dashboard Cite

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“…For consistency with the ESP calculations, the multipole moments were derived from a distributed multipole analysis (29) of the B3LYP/ 6-31G(d,p) isolated-molecule charge density. The minimised crystal structures were clustered by comparing their simulated X-ray powder diffraction patterns and molecular coordination spheres (44) and the 20 most stable, distinct crystal structures reminimized using Crystal Optimizer (45), a substantially revised version of DMAflex (46) to account for the effect of molecular flexibility on the relative lattice energy of the predicted crystal structures. This procedure simultaneously minimized the cell angles, cell lengths, the position and orientation of the molecules in the asymmetric unit and all torsion angles with the exception of torsions defining methylene and aromatic hydrogen atoms.…”

Section: Methodsmentioning

confidence: 99%

Tunable recognition of the steroid α-face by adjacent π-electron density

Friščić

Lancaster

Fábián

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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We report a previously unknown recognition motif between the α-face of the steroid hydrocarbon backbone and π-electron-rich aromatic substrates. Our study is based on a systematic and comparative analysis of the solid-state complexation of four steroids with 24 aromatic molecules. By using the solid state as a medium for complexation, we circumvent solubility and solvent competition problems that are inherent to the liquid phase. Characterization is performed using powder and single crystal X-ray diffraction, infrared solid-state spectroscopy and is complemented by a comprehensive cocrystal structure prediction methodology that surpasses earlier computational approaches in terms of realism and complexity. Our combined experimental and theoretical approach reveals that the α⋯π stacking is of electrostatic origin and is highly dependent on the steroid backbone's unsaturated and conjugated character. We demonstrate that the α⋯π stacking interaction can drive the assembly of molecules, in particular progesterone, into solid-state complexes without the need for additional strong interactions. It results in a marked difference in the solid-state complexation propensities of different steroids with aromatic molecules, suggesting a strong dependence of the steroid-binding affinity and even physicochemical properties on the steroid's A-ring structure. Hence, the hydrocarbon part of the steroid is a potentially important variable in structure-activity relationships for establishing the binding and signaling properties of steroids, and in the manufacture of pharmaceutical cocrystals.steroids | molecular recognition | mechanosynthesis | crystal structure prediction A wide variety of tools, including site-directed mutagenesis (1), binding and inhibition screening (2-4), computational modeling (5), and protein crystallography (6, 7) are commonly used in studying the interaction of biologically important molecules, such as steroids, with their respective receptor binding domains (8). Molecular recognition can also be effectively probed using the considerably simpler and inexpensive methodology of forming crystalline molecular complexes (9) (multicomponent crystals, also known as cocrystals). Similarly to binding on synthetic model receptors (10, 11), solid-state complexation with small molecules (cocrystallization) offers the possibility to separately screen and deconvolute a far larger space of molecular recognition motifs (12) that collectively account for the biological activity. However, formation of solid-state complexes from solution is not a reliable measure of molecular affinity due to the vexatious problems of solubility and solvent competition. Mechanosynthesis, in the form of liquid-assisted grinding (LAG) (13), avoids these adverse effects and offers the possibility to systematically explore molecular recognition in the solid state (14,15).The rationalization of statistical data from structure-activity binding (16) or cocrystallization studies (17) is often based on qualitative and intuitive arguments and an establ...

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“…In this work, we use the method of Day et al 21 , and that of Chisholm and Motherwell 73 . In the former, the structural difference is described by the RMS (root mean square)…”

Section: Periodic Calculations: Structurementioning

confidence: 99%

Force Fields and Point Charges for Crystal Structure Modeling

Svärd

Rasmuson

2009

Ind. Eng. Chem. Res.

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ABSTRACT. Molecular simulation is increasingly used by chemical engineers and industrial chemists in process and product development. In particular, the possibility to predict the structure and stability of potential polymorphs of a substance is of tremendous interest to the pharmaceutical and specialty chemicals industry. Molecular mechanics modelling relies on the use of parameterized force fields and methods of assigning point charges to the atoms in the molecules. In commercial molecular simulation software a wide variety of such combinations are available, and there is a need for critical assessment of the capabilities of the different alternatives.In the present work, the performance of several molecular mechanics force fields combined with different methods for the assignment of atomic point charges have been examined with regard to their ability to calculate absolute crystal lattice energies and their capacity to identify the experimental structure as a minimum on the potential energy hypersurface. Seven small, aromatic mono-molecular crystalline compounds are used in the evaluation. It is found that the majority of the examined methods cannot be used to reliably predict absolute lattice energies. The most promising results were obtained with the Pcff force field using integral charges, and the Dreiding force field using Gasteiger charges, both of which performed with an accuracy of the same order of magnitude as the variations in experimental lattice energies. Overall, it has been observed that the best results are achieved if the same force field method is used to relax the crystal structure and calculate the energy, and to optimize and calculate the energy of the gas phase molecule used for the correction for changes in molecular geometry. The Pcff and Compass force fields with integral charges have been found to predict relaxed structures closest to the experimental ones.In addition, five different methods for determining point charges fitted to the electrostatic potential (ESP-charges), available in the same software, have been evaluated. For each method, the molecular geometries of ten small, organic molecules were optimized, and ESP-charges calculated and analyzed for linear correlation with a set of reference charges of an accepted standard method, HF/6-31G*. Dmol-3 gives charges that correlate well with the reference charge.The charges from Vamp are not linearly scalable to the HF/6-31G*-level, which is attributed partly to the geometry optimization but mainly to the calculation of the ESP and the subsequent charge fit.

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COMPACK: a program for identifying crystal structure similarity using distances

Cited by 333 publications

References 9 publications

A fingerprint based metric for measuring similarities of crystalline structures

A fingerprint based metric for measuring similarities of crystalline structures

Tunable recognition of the steroid α-face by adjacent π-electron density

Force Fields and Point Charges for Crystal Structure Modeling

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