Mercury 4.0: from visualization to analysis, design and prediction.

The program Mercury, developed at the Cambridge Crystallographic Data Centre, was originally designed primarily as a crystal structure visualization tool. Over the years the fields and scientific communities of chemical crystallography and crystal engineering have developed to require more advanced structural analysis software. Mercury has evolved alongside these scientific communities and is now a powerful analysis, design and prediction platform which goes a lot further than simple structure visualization.

Validating and understanding ring conformations using small molecule crystallographic data.

Understanding the conformational preferences of ring structures is fundamental to structure-based drug design. Although the Cambridge Structural Database (CSD) provides information on the preferred conformations of small molecules, analyzing this data can be very time-consuming. In order to overcome this hurdle, tools have been developed for quickly extracting geometrical preferences from the CSD. Here we describe how the program Mogul has been extended to analyze and compare ring conformations, using a library derived from over 900 000 ring fragments in the CSD. We illustrate how these can be used to understand the conformational preferences of molecules in a crystal lattice and bound to proteins.

Incorporating partial matches within multi-objective pharmacophore identification.

This paper describes the extension of our earlier multi-objective method for generating plausible pharmacophore hypotheses to incorporate partial matches. Diverse sets of molecules rarely adopt exactly the same binding mode, and so allowing the identification of partial matches allows our program to be applied to larger and more diverse datasets. The method explores the conformational space of a series of ligands simultaneously with their alignment using a multi-objective genetic algorithm (MOGA). The principles of Pareto ranking are used to evolve a diverse set of pharmacophore hypotheses that are optimised on conformational energy of the ligands, the goodness of the overlay and the volume of the overlay. A partial match is defined as a pharmacophoric feature that is present in at least two, but not all, of the ligands in the set. The number of ligands that map to a given pharmacophore point is taken into account when evaluating an overlay. The method is applied to a number of test cases extracted from the Protein Data Bank (PDB) where the true overlay is known.

Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques.

Pharmacophore methods provide a way of establishing a structure activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation.