title: Assessing the predictive power of unsupervised visualization techniques to improve the identification of GPCR-focused compound libraries.
abstract: Principal component analysis and self-organizing maps (SOMs) were compared to cluster and visualize the chemical space of a large and diverse data set. The data set comprised about 3000 G-protein-coupled receptor (GPCR) ligands for about 130 receptors and 3000 non-GPCR ligands from the World Drug Index. The molecules were described with a topological pharmacophore point histogram descriptor and a chemical fingerprint descriptor. To assess the predictive power of the clustering, a leave-multiple-out cross validation with k nearest neighbor classification was performed. The results of the classification tests and the visualization showed a clear superiority of the SOM method. SOM correctly divided the data set into two main clusters, one for the GPCR and the other for the non-GPCR ligands. Our results suggest that a continuous GPCR-ligand space exists.
authors.affiliation: Actelion Pharmaceuticals Ltd., Gewerbestrasse 16, CH-4123 Allschwil, Switzerland. modest.korff@actelion.com
authors.forename: Modest
authors.initials: M
authors.lastname: von Korff
authors.forename: Kurt
authors.initials: K
authors.lastname: Hilpert
mesh_terms: D015195:Drug Design; D043562:Receptors, G-Protein-Coupled / Q000187:drug effects*
chemical_list: D043562:Receptors, G-Protein-Coupled