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Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.

Liu LL, Lu J, Lu Y, Zheng MY, Luo XM, Zhu WL, Jiang HL, Chen KX - Acta Pharmacol. Sin. (2014)

Bottom Line: The models were internally validated with the training set of compounds, and then applied to the test set for validation.Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.

View Article: PubMed Central - PubMed

Affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

ABSTRACT

Aim: A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.

Methods: Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.

Results: A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.

Conclusion: The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.

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Related in: MedlinePlus

Example to show the ECFP iterative generation procedure.
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fig1: Example to show the ECFP iterative generation procedure.

Mentions: Extended-connectivity fingerprints (ECFPs) are a class of 2D fingerprints for capturing molecular features based on a variant of the Morgan algorithm16. An example of the generation process of ECFP for benzamide is shown in Figure 1. The ECFPs' capture atom information based on the Daylight atomic invariants rule17 and the identifiers are hashed into a single 32-bit integer value18. The number of iterations performed is determined by the maximum diameter of the neighborhoods requested. For example, “ECFP_4” generates features around each atom up to a diameter of 4, which requires two iterations. We also calculated the fingerprints with the “Calculate Molecular Properties” protocol within the Discovery Studio (version 3.0; Accelrys, San Diego, CA, USA).


Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.

Liu LL, Lu J, Lu Y, Zheng MY, Luo XM, Zhu WL, Jiang HL, Chen KX - Acta Pharmacol. Sin. (2014)

Example to show the ECFP iterative generation procedure.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4125710&req=5

fig1: Example to show the ECFP iterative generation procedure.
Mentions: Extended-connectivity fingerprints (ECFPs) are a class of 2D fingerprints for capturing molecular features based on a variant of the Morgan algorithm16. An example of the generation process of ECFP for benzamide is shown in Figure 1. The ECFPs' capture atom information based on the Daylight atomic invariants rule17 and the identifiers are hashed into a single 32-bit integer value18. The number of iterations performed is determined by the maximum diameter of the neighborhoods requested. For example, “ECFP_4” generates features around each atom up to a diameter of 4, which requires two iterations. We also calculated the fingerprints with the “Calculate Molecular Properties” protocol within the Discovery Studio (version 3.0; Accelrys, San Diego, CA, USA).

Bottom Line: The models were internally validated with the training set of compounds, and then applied to the test set for validation.Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.

View Article: PubMed Central - PubMed

Affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

ABSTRACT

Aim: A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.

Methods: Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.

Results: A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.

Conclusion: The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.

Show MeSH
Related in: MedlinePlus