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Homology modelling of metabotropic glutamate receptor 2., , , , and . J. Cheminformatics, 3 (S-1): 40 (2011)Multiple conformational states in retrospective virtual screening - homology models vs. crystal structures: beta-2 adrenergic receptor case study., , , , and . J. Cheminformatics, (2015)Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods - A Case Study of Serotonin Receptors 5-HT6 and 5-HT7., , , , , and . Journal of Chemical Information and Modeling, 55 (4): 823-832 (2015)From Homology Models to a Set of Predictive Binding Pockets-a 5-HT1A Receptor Case Study., , , , , , , , , and . J. Chem. Inf. Model., 57 (2): 311-321 (2017)GPCRdb: an information system for G protein-coupled receptors., , , , , , , , , and . Nucleic Acids Res., 44 (Database-Issue): 356-364 (2016)The importance of template choice in homology modeling. A 5-HT6R case study., , , , and . J. Cheminformatics, 5 (S-1): 8 (2013)GPCRdb in 2018: adding GPCR structure models and ligands., , , , , , , and . Nucleic Acids Res., 46 (Database-Issue): D440-D446 (2018)Impact of Template Choice on Homology Model Efficiency in Virtual Screening., , , , and . J. Chem. Inf. Model., 54 (6): 1661-1668 (2014)GPCRdb in 2021: integrating GPCR sequence, structure and function., , , , , , , and . Nucleic Acids Res., 49 (Database-Issue): D335-D343 (2021)Rapid binding site analysis by means of structural interaction fingerprint patterns - an implication to GPCR-targeted CADD., , and . J. Cheminformatics, 3 (S-1): 42 (2011)