hERG me out.
A detailed analysis of the hERG content inside the ChEMBL database is performed. The correlation between the outcome from binding assays and functional assays is probed. On the basis of descriptor distributions, design paradigms with respect to structural and physicochemical properties of hERG active and hERG inactive compounds are challenged. Finally, classification models with different data sets are trained. All source code is provided, which is based on the Python open source packages RDKit and scikit-learn to enable the community to rerun the experiments. The code is stored on github ( https://github.com/pzc/herg_chembl_jcim).
Merck KGaA , Small Molecule Platform, Global Computational Chemistry, Frankfurter Strasse 250, 64293 Darmstadt, Germany.
Paul
P
Czodrowski
D019295:Computational Biology; D030562:Databases, Protein; D051638:Ether-A-Go-Go Potassium Channels / Q000378:metabolism*; D006801:Humans; D012984:Software
D051638:Ether-A-Go-Go Potassium Channels