A binary QSAR model for classification of hERG potassium channel blockers

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• 作者：Khac-Minh Thai ; Gerhard F. Ecker
• 关键词：hERG ; Potassium channel ; Binary QSAR ; VSA descriptors ; GH score
• 刊名：Bioorganic and Medicinal Chemistry
• 出版年：2008
• 期刊代码：9_09680896
• 类别：ch
• 出版时间：1 April 2008
• 卷：16
• 期：7
• 页码：4107-4119
• 文件大小：491 K

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 IC₅₀ = 1 and of 10 μM, 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.