Combining Global and Local Measures for Structure-Based Druggability Predictions

By Andrea Volkamer, Daniel Kuhn, T. Grombacher, Friedrich Rippmann, Matthias Rarey. Journal of Chemical Information and Modeling, 2012

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DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment (2012, unknown): “(Volkamer et al., 2012) For each input structure, the method predicts potential pockets, describes them through descriptors and queries the SVM model for druggability estimations.”
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. (2018, methodology): “SVMs have been used for predicting druggability scores for targets (48).”
Key Topics in Molecular Docking for Drug Design (2019, background): “DoGSiteScor r is an algorithm that determines possible pockets and their ruggability scores, which describe the potential of the binding site to interact with a small drug-like molecul [13].”
BRENDA in 2019: a European ELIXIR core data resource (2018, background): “Enzyme 3D structures linked with Protoss and DoGSiteScorer Links to DoGSiteScorer (28) (‘enzyme pockets’) and Protoss (29) (‘protonated enzyme’), developed by the Rarey group at the Center of Bioinformatics, University of Hamburg were integrated into the 3D-structure search result pages in release 2”