hERG classification model based on a combination of support vector machine method and GRIND descriptors

Qiyuan Li, Flemming Steen Jorgensen, Tudor Oprea, Søren Brunak, Olivier Taboureau

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    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 earlystages 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 mu m 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 urn 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 (hftp://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.
    Original languageEnglish
    JournalMolecular Pharmaceutics
    Volume5
    Issue number1
    Pages (from-to)117-127
    ISSN1543-8384
    DOIs
    Publication statusPublished - 2008

    Keywords

    • support vector machine
    • classification model
    • human Ether-a-go-go Related Gene
    • GRIND descriptors

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