title: Combining global and local measures for structure-based druggability predictions.
abstract: Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction.
authors.affiliation: University of Hamburg, Center for Bioinformatics, Bundesstr. 43, 20146 Hamburg, Germany.
authors.forename: Andrea
authors.initials: A
authors.lastname: Volkamer
authors.forename: Daniel
authors.initials: D
authors.lastname: Kuhn
authors.forename: Thomas
authors.initials: T
authors.lastname: Grombacher
authors.forename: Friedrich
authors.initials: F
authors.lastname: Rippmann
authors.forename: Matthias
authors.initials: M
authors.lastname: Rarey
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D015195:Drug Design; D055808:Drug Discovery; D008024:Ligands; D004364:Pharmaceutical Preparations; D060388:Support Vector Machine
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations