title: Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model.
abstract: A QSAR based predictive model of hERG activity in terms of 'global descriptors' has been developed and evaluated. The QSAR was developed by training 77 compounds covering a wide range of activities and was validated based on an external 'test set' of 80 compounds using neural network method. Statistical parameters and examination of enrichment factor indicated the effectiveness of the present model. Randomization test demonstrated the robustness of the model and cross-validation test further validated the QSAR. Domain of applicability test indicated to the high degree of reliability of the predicted results. Satisfactory performance in classifying compounds into 'active' and 'inactive' groups was also obtained. The cases where the QSAR failed, the possible sources of errors have been discussed.
authors.affiliation: Molecular Modeling Section, Biolab, Chembiotek, TCG Lifesciences Ltd., Bengal Intelligent Park, Tower-B, 2nd Floor, Block-EP & GP, Sector-V, Salt Lake Electronic Complex, Calcutta-700091, India. nandita@chembiotek.com
authors.forename: Nandita
authors.initials: N
authors.lastname: Sinha
authors.forename: Srikanta
authors.initials: S
authors.lastname: Sen
mesh_terms: D003198:Computer Simulation; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D015394:Molecular Structure; D009930:Organic Chemicals / Q000737:chemistry* / Q000494:pharmacology; D021281:Quantitative Structure-Activity Relationship
chemical_list: D051638:Ether-A-Go-Go Potassium Channels; D009930:Organic Chemicals