Docking and scoring in virtual screening for drug discovery: methods and applications.

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.

Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality.

A novel compound classification algorithm is described that operates in binary molecular descriptor spaces and groups active compounds together in a computationally highly efficient manner. The method involves the transformation of continuous descriptor value ranges into a binary format, subsequent definition of simplified descriptor spaces, identification of consensus positions of specific compound sets in these spaces, and iterative adjustments of the dimensionality of the descriptor spaces in order to discriminate compounds sharing similar activity from others. We term this approach Dynamic Mapping of Consensus positions (DMC) because the definition of reference spaces is tuned toward specific compound classes and their dimensionality is increased as the analysis proceeds. When applied to virtual screening, sets of bait compounds are added to a large screening database to identify hidden active molecules. In these calculations, molecules that map to consensus positions after elimination of most of the database compounds are considered hit candidates. In a benchmark study on five biological activity classes, hits for randomly assembled sets of bait molecules were correctly identified in 95% of virtual screening calculations in a source database containing more than 1.3 million molecules, thus providing a measure of the sensitivity of the DMC technique.

Recursive median partitioning for virtual screening of large databases.

Recently, we have introduced the median partitioning (MP) method for diversity selection and compound classification. The MP approach utilizes property descriptors with continuous value ranges, transforms these descriptors into a binary classification scheme by determining their medians in source databases, and divides database molecules in subsequent steps into populations above or below these medians. Having previously demonstrated the usefulness of MP for the classification of molecules according to biological activity, we have now gone a step further and extended the methodology for application in virtual screening. In these calculations, a series of bait molecules having desired activity is added to large compound databases, and subsequent iterations or recursions are carried out to reduce the number of candidate molecules until a small number of compounds are found in partitions enriched with bait molecules. For each recursion step, descriptor combinations are identified that copartition as many active molecules as possible. Descriptor selection is facilitated by application of a genetic algorithm (GA). The recursive MP approach (RMP) has been applied to five diverse biological activity classes in virtual screening of a database consisting of approximately 1.34 million molecules to which different types of active compounds were added. RMP analysis produced hit rates of up to 21%, dependent on the biological activity class, and led to an average approximately 3600-fold improvement over random selection for the activity classes that were used as test cases.