Patrick McCabe

Mercury CSD 2.0 – new features for the visualization and investigation of crystal structures

The program Mercury, developed by the Cambridge Crystallographic Data Centre, is designed primarily as a crystal structure visualization tool. A new module of functionality has been produced, called the Materials Module, which allows highly customizable searching of structural databases for intermolecular interaction motifs and packing patterns. This new module also includes the ability to perform packing similarity calculations between structures containing the same compound. In addition to the Materials Module, a range of further enhancements to Mercury has been added in this latest release, including void visualization and links to ConQuest, Mogul and IsoStar.

Mercury: visualization and analysis of crystal structures

Since its original release, the popular crystal structure visualization program Mercury has undergone continuous further development. Comparisons between crystal structures are facilitated by the ability to display multiple structures simultaneously and to overlay them. Improvements have been made to many aspects of the visual display, including the addition of depth cueing, and highly customizable lighting and background effects. Textual and numeric data associated with structures can be shown in tables or spreadsheets, the latter opening up new ways of interacting with the visual display. Atomic displacement ellipsoids, calculated powder diffraction patterns and predicted morphologies can now be shown. Some limited molecular-editing capabilities have been added. The object-oriented nature of the C++ libraries underlying Mercury makes it easy to re-use the code in other applications, and this has facilitated three-dimensional visualization in several other programs produced by the Cambridge Crystallographic Data Centre.

Report on the sixth blind test of organic crystal structure prediction methods

The results of the sixth blind test of organic crystal structure prediction methods are presented and discussed, highlighting progress for salts, hydrates and bulky flexible molecules, as well as on-going challenges.

New software for statistical analysis of Cambridge Structural Database data

A new piece of software for statistical analysis of geometrical, chemical and crystallographic data within the Cambridge Structural Database System is described. This software has been written specifically to deal with chemical structure data and crucially provides simultaneous visualization of the three-dimensional structural information.

Knowledge-based libraries for predicting the geometric preferences of druglike molecules.

We describe the automated generation of libraries for predicting the geometric preferences of druglike molecules. The libraries contain distributions of molecular dimensions based on crystal structures in the Cambridge Structural Database (CSD). Searching of the libraries is performed in cascade fashion to identify the most relevant distributions in cases where precise structural features are poorly represented by existing crystal structures. The libraries are fully comprehensive for bond lengths, valence angles, and rotamers and produce templates for the large majority of unfused and fused rings. Geometry distributions for rotamers and rings take into account any atom chirality that may be present. Library validation has been performed on a set of druglike molecules whose structures were published after the latest CSD entry contributing to the libraries. Hence, the validation gives a true indication of prediction accuracy.

Generation of crystal structures using known crystal structures as analogues

An investigation into using shape-similarity of molecules to generate putative crystal structures.

The ensemble performance index: an improved measure for assessing ensemble pose prediction performance.

We present a theoretical study on the performance of ensemble docking methodologies considering multiple protein structures. We perform a theoretical analysis of pose prediction experiments which is completely unbiased, as we make no assumptions about specific scoring functions, search paradigms, protein structures, or ligand data sets. We introduce a novel interpretable measure, the ensemble performance index (EPI), for the assessment of scoring performance in ensemble docking, which will be applied to simulated and real data sets.

Kernel density estimation applied to bond length, bond angle, and torsion angle distributions.

We describe the method of kernel density estimation (KDE) and apply it to molecular structure data. KDE is a quite general nonparametric statistical method suitable even for multimodal data. The method generates smooth probability density function (PDF) representations and finds application in diverse fields such as signal processing and econometrics. KDE appears to have been under-utilized as a method in molecular geometry analysis, chemo-informatics, and molecular structure optimization. The resulting probability densities have advantages over histograms and, importantly, are also suitable for gradient-based optimization. To illustrate KDE, we describe its application to chemical bond length, bond valence angle, and torsion angle distributions and show the ability of the method to model arbitrary torsion angle distributions.

Knowledge-Based Optimization of Molecular Geometries Using Crystal Structures

This paper describes a novel way to use the structural information contained in the Cambridge Structural Database (CSD) to drive geometry optimization of organic molecules. We describe how CSD structural information is transformed into objective functions for gradient-based optimization to provide good quality geometries for a large variety of organic molecules. Performance is assessed by minimizing different sets of organic molecules reporting RMSD movements for bond lengths, valence angles, torsion angles, and heavy atom positions.

Use of crystal structure informatics for defining the conformational space needed for predicting crystal structures of pharmaceutical molecules

Determining the range of conformations that a flexible pharmaceutical-like molecule could plausibly adopt in a crystal structure is a key to successful crystal structure prediction (CSP) studies. We aim to use conformational information from the crystal structures in the Cambridge Structural Database (CSD) to facilitate this task. The conformations produced by the CSD Conformer Generator are reduced in number by considering the underlying rotamer distributions, an analysis of changes in molecular shape, and a minimal number of molecular ab initio calculations. This method is tested for five pharmaceutical-like molecules where an extensive CSP study has already been performed. The CSD informatics-derived set of crystal structure searches generates almost all the low-energy crystal structures previously found, including all experimental structures. The workflow effectively combines information on individual torsion angles and then eliminates the combinations that are too high in energy to be found in the solid state, reducing the resources needed to cover the solid-state conformational space of a molecule. This provides insights into how the low-energy solid-state and isolated-molecule conformations are related to the properties of the individual flexible torsion angles.