Identification and Classification of GPCR Ligands Using Self-Organizing Neural Networks

摘要

A combination of a 3D descriptor and neural networks was applied to model the relationship between molecular structures and their activity as G-Protein-Coupled Receptor, GPCR ligands. The 3D descriptor, namely the Radial Distribution Function, RDF, is based on the interatomic distances and thus expresses the molecule's pharmaco-phoric features. The first goal of the study was to analyze whether the RDF code provides sufficient GPCR relevant structural information to separate GPCR ligands from a set of randomly selected molecules. Cluster experiments with neural networks show a clear separation of these two classes and even a separation between different classes of GPCR ligands. In a second series of experiments neural networks were used to predict a GPCR-ligand-likeness score. Based on this score 71% of the GPCR ligands (in a data set with only 5.9% active compounds) were correctly identified in a cross-validation experiment.