Skip to main content

Winnow based identification of potent hERG inhibitors in silico: comparative assessment on different datasets

Due to the potentially lethal effects of potent hERG inhibition, in silico approaches which can identify potent (IC50 < 1 µM) inhibitors are of considerable interest to the pharmaceutical industry [1]. We present recent work [2] in which in silico binary classifiers were trained to discriminate potent inhibitors from compounds exhibiting weaker (IC50 ≥ 1 µM) inhibition. Initial models were based on a version of the memory efficient Winnow algorithm [3]. These initial models were generated using various descriptor sets. The descriptor set yielding the best cross-validated initial Winnow model was used to build models using each of Winnow, Random Forest and Support Vector Machine. Analysis of the contributions of different substructural and physiochemical features in the final Winnow models indicates they may be interpreted, albeit with caution. All final models were externally validated, with no algorithm consistently outperforming the others. These approaches were directly compared, on various datasets, to those proposed by Thai and Ecker [4] and by Dubus et al. [5]. The results indicate that the Winnow models are competitive with earlier approaches proposed in the literature. The findings also emphasise a potential difficulty when seeking to estimate the predictive power of in silico models on small quantities of data: model performance may vary considerably, particularly when training and validating on different datasets.


  1. Gavaghan CL, Hasselgren Arnby C, Blomberg N, Strandlund G, Boyer S: Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. J Comput-Aided Mol Des. 2007, 21: 189-206. 10.1007/s10822-006-9095-6.

    Article  CAS  Google Scholar 

  2. Marchese Robinson RL, Glen RC, Mitchell JBO: Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results. Mol Inf. 2011, 30: 443-458. 10.1002/minf.201000159.

    Article  CAS  Google Scholar 

  3. Nigsch F, Mitchell JBO: How To Winnow Actives from Inactives: Introducing Molecular Orthogonal Sparse Bigrams (MOSBs) and Multiclass Winnow. J Chem Inf Model. 2008, 48: 306-318. 10.1021/ci700350n.

    Article  CAS  Google Scholar 

  4. Thai KM, Ecker GF: A binary QSAR model for classification of hERG potassium channel blockers. Bioorg Med Chem. 2008, 16: 4107-4119. 10.1016/j.bmc.2008.01.017.

    Article  CAS  Google Scholar 

  5. Dubus E, Ijjaali I, Petitet F, Michel A: In Silico Classification of hERG Channel Blockers: a Knowledge-Based Strategy. ChemMedChem. 2006, 1 (6): 622-630. 10.1002/cmdc.200500099.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Richard L Marchese Robinson.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Marchese Robinson, R.L., Glen, R.C. & Mitchell, J.B. Winnow based identification of potent hERG inhibitors in silico: comparative assessment on different datasets. J Cheminform 4 (Suppl 1), O6 (2012).

Download citation

  • Published:

  • DOI: