A binary classification model combining GRIND descriptors with an SVM classifier achieved up to 94% overall accuracy (MCC 0.86) in discriminating between hERG blockers and nonblockers.
An in silico SVM-based model using GRIND descriptors can effectively filter potential hERG channel inhibitors in early drug discovery, showing improved prediction accuracy over existing methods.
Effect estimate: MCC 0.86
The human Ether-a-go-go Related Gene (hERG) potassium channel is one of the major critical factors associated with QT interval prolongation and development of arrhythmia called Torsades de Pointes (TdP). It has become a growing concern of both regulatory agencies and pharmaceutical industries who invest substantial effort in the assessment of cardiac toxicity of drugs. The development of in silico tools to filter out potential hERG channel inhibitors in early stages of the drug discovery process is of considerable interest. Here, we describe binary classification models based on a large and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 microm and achieved an overall accuracy up to 94% with a Matthews coefficient correlation (MCC) of 0.86 ( F-measure of 0.90 for blockers and 0.95 for nonblockers). The model at a 40 microm threshold showed the best performance and was validated internally (MCC of 0.40 and F-measure of 0.57 for blockers and 0.81 for nonblockers, using a leave-one-out cross-validation). On an external set of 66 compounds, 72% of the set was correctly predicted ( F-measure of 0.86 and 0.34 for blockers and nonblockers, respectively). Finally, the model was also tested on a large set of hERG bioassay data recently made publicly available on PubChem ( http://pubchem.ncbi.nlm.nih.gov/assay/assay.cgi?aid=376) to achieve about 73% accuracy ( F-measure of 0.30 and 0.83 for blockers and nonblockers, respectively). Even if there is still some limitation in the assessment of hERG blockers, the performance of our model shows an improvement between 10% and 20% in the prediction of blockers compared to other methods, which can be useful in the filtering of potential hERG channel inhibitors.
Li et al. (Wed,) conducted a other in hERG channel inhibition (n=495). Binary classification model (SVM and GRIND descriptors) was evaluated on Discrimination between hERG blockers and nonblockers (MCC 0.86). A binary classification model combining GRIND descriptors with an SVM classifier achieved up to 94% overall accuracy (MCC 0.86) in discriminating between hERG blockers and nonblockers.