title: Improving the search performance of extended connectivity fingerprints through activity-oriented feature filtering and application of a bit-density-dependent similarity function.
abstract: The Pipeline Pilot extended connectivity fingerprints (ECFPs) are currently among the most popular similarity search tools in drug discovery settings. ECFPs do not have a fixed bit string format but generate variable numbers of structural features for individual test molecules. This variable string design makes ECFP representations amenable to compound-class-directed modification. We have devised an intuitive feature-filtering technique that focuses ECFP search calculations on feature string ensembles of given compound activity classes. In combination with a simple bit-density-dependent similarity function, feature filtering consistently improved the search performance of ECFP calculations based on Tanimoto similarity and state-of-the-art data fusion techniques on a diverse array of activity classes. Feature filtering and the bit density similarity metric are easily implemented in the Pipeline Pilot environment. The approach provides a viable alternative to conventional similarity searching and should be of general interest to further improve the success rate of practical ECFP applications.
authors.affiliation: Department of Life Science Informatics, B-IT, LIMES, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany.
authors.forename: Ye
authors.initials: Y
authors.lastname: Hu
authors.forename: Eugen
authors.initials: E
authors.lastname: Lounkine
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D020650:Combinatorial Chemistry Techniques; D003198:Computer Simulation; D013329:Structure-Activity Relationship