Comparison of correlation vector methods for ligand-based similarity searching.

Correlation vector methods were tested for their usefulness in ligand-based virtual screening. Three molecular descriptors--two based on potential pharmacophore points and one on partial atom charges--and three similarity measures--the Manhattan distance, the Euclidian distance and the Tanimoto coefficient--were compared. The alignment-free descriptors seem to be particularly applicable when a course-grain filtering of data sets is required in combination with a high execution speed. Significant enrichment of actives was obtained by retrospective analysis. The cumulative percentages for all three descriptors allow for the retrieval of up to 78% of the active molecules in the first five percent of the reference database. Different descriptors retrieved only weakly overlapping sets of active molecules among the top-ranking compounds. If a single similarity index is to be used, the Manhattan distance seems to be particularly applicable. Generally, none of the three different descriptors tested in this study clearly outperformed the others. The suitability of a descriptor critically depends on the ligand-receptor interaction under investigation. For ligand-based similarity searching it is recommended to exploit several descriptors in parallel.