Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model

Eur J Med Chem. 2011 Feb;46(2):618-30. doi: 10.1016/j.ejmech.2010.11.042. Epub 2010 Dec 8.


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.

MeSH terms

  • Computer Simulation*
  • Ether-A-Go-Go Potassium Channels / antagonists & inhibitors
  • Ether-A-Go-Go Potassium Channels / chemistry*
  • Ether-A-Go-Go Potassium Channels / metabolism
  • Humans
  • Molecular Structure
  • Organic Chemicals / chemistry*
  • Organic Chemicals / pharmacology
  • Quantitative Structure-Activity Relationship*


  • Ether-A-Go-Go Potassium Channels
  • Organic Chemicals