Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier.
A novel technique for similarity searching is introduced. Molecules are represented by atom environments, which are fed into an information-gain-based feature selection. A naïve Bayesian classifier is then employed for compound classification. The new method is tested by its ability to retrieve five sets of active molecules seeded in the MDL Drug Data Report (MDDR). In comparison experiments, the algorithm outperforms all current retrieval methods assessed here using two- and three-dimensional descriptors and offers insight into the significance of structural components for binding.
Bender
Andreas
A
Unilever Centre for Molecular Science Informatics, Chemistry Department, University of Cambridge, Cambridge CB2 1EW, UK. ab454@cam.ac.uk
Mussa
Hamse Y
HY
Glen
Robert C
RC
Reiling
Stephan
S