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.
['Jeffrey W. Godden', 'John R. Furr', 'Ling Xue', 'Florence L. Stahura', 'Juergen Bajorath']
Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality.