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.