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

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

hERG classification model based on a combination of support vector machine method and GRIND descriptors. / Li, Qiyuan; Jørgensen, Flemming Steen; Oprea, Tudor; Brunak, Søren; Taboureau, Olivier.

In: Molecular Pharmaceutics, Vol. 5, No. 1, 2012, p. 117-127.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Li, Q, Jørgensen, FS, Oprea, T, Brunak, S & Taboureau, O 2012, 'hERG classification model based on a combination of support vector machine method and GRIND descriptors', Molecular Pharmaceutics, vol. 5, no. 1, pp. 117-127. https://doi.org/10.1021/mp700124e

APA

Li, Q., Jørgensen, F. S., Oprea, T., Brunak, S., & Taboureau, O. (2012). hERG classification model based on a combination of support vector machine method and GRIND descriptors. Molecular Pharmaceutics, 5(1), 117-127. https://doi.org/10.1021/mp700124e

Vancouver

Li Q, Jørgensen FS, Oprea T, Brunak S, Taboureau O. hERG classification model based on a combination of support vector machine method and GRIND descriptors. Molecular Pharmaceutics. 2012;5(1):117-127. https://doi.org/10.1021/mp700124e

Author

Li, Qiyuan ; Jørgensen, Flemming Steen ; Oprea, Tudor ; Brunak, Søren ; Taboureau, Olivier. / hERG classification model based on a combination of support vector machine method and GRIND descriptors. In: Molecular Pharmaceutics. 2012 ; Vol. 5, No. 1. pp. 117-127.

Bibtex

@article{55a95100dbdc11dcbee902004c4f4f50,
title = "hERG classification model based on a combination of support vector machine method and GRIND descriptors",
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 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.",
keywords = "Computer Simulation, Ether-A-Go-Go Potassium Channels, Humans, Models, Molecular, Models, Theoretical, Potassium Channel Blockers, Quantitative Structure-Activity Relationship, Sensitivity and Specificity",
author = "Qiyuan Li and J{\o}rgensen, {Flemming Steen} and Tudor Oprea and S{\o}ren Brunak and Olivier Taboureau",
year = "2012",
doi = "10.1021/mp700124e",
language = "English",
volume = "5",
pages = "117--127",
journal = "Molecular Pharmaceutics",
issn = "1543-8384",
publisher = "American Chemical Society",
number = "1",

}

RIS

TY - JOUR

T1 - hERG classification model based on a combination of support vector machine method and GRIND descriptors

AU - Li, Qiyuan

AU - Jørgensen, Flemming Steen

AU - Oprea, Tudor

AU - Brunak, Søren

AU - Taboureau, Olivier

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Computer Simulation

KW - Ether-A-Go-Go Potassium Channels

KW - Humans

KW - Models, Molecular

KW - Models, Theoretical

KW - Potassium Channel Blockers

KW - Quantitative Structure-Activity Relationship

KW - Sensitivity and Specificity

U2 - 10.1021/mp700124e

DO - 10.1021/mp700124e

M3 - Journal article

C2 - 18197627

VL - 5

SP - 117

EP - 127

JO - Molecular Pharmaceutics

JF - Molecular Pharmaceutics

SN - 1543-8384

IS - 1

ER -

ID: 2753098