A binary QSAR model for classification of hERG potassium channel blockers.

Thai KM; Ecker GF

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A binary QSAR model for classification of hERG potassium channel blockers.

机译：A binary QSAR model for classification of hERG potassium channel blockers.

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摘要

Acquired long QT syndrome causes severe cardiac side effects and represents a major problem in clinical studies of drug candidates. One of the reasons for development of arrhythmias related to long QT is inhibition of the human ether-a-go-go-related-gene (hERG) potassium channel. Therefore, early prediction of hERG K(+) channel affinity of drug candidates is becoming increasingly important in the drug discovery process. Binary QSAR models with threshold values at IC(50)=1 and of 10 microM, respectively, were generated using two different sets of descriptors. One set comprising 32 P_VSA descriptors and the other one utilizing a set of descriptors identified out of a large set via a feature selection algorithm. For the full dataset, the power for classification of hERG blockers was 82-88, which meets prior classification models. Considering the fact that 2D descriptors are fast and easy to calculate, these binary QSAR models are versatile tools for use in virtual screening protocols.

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《Bioorganic and medicinal chemistry》 |2008年第7期|4107-4119|共13页
作者
Thai KM; Ecker GF;
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作者单位

Emerging Field Pharmacoinformatics, Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria.;

ost.saga-med.ac.jp;

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正文语种英语
中图分类药学;生物化学;
关键词
Animals; Cell Line; Cercopithecus aethiops; Cricetinae; Ether-A-Go-Go Potassium Channels; 动物; 细胞系; 长尾猴; 黑; 模型; 生物学; 分子结构;

A binary QSAR model for classification of hERG potassium channel blockers.

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