A k-nearest neighbor classification of hERG K+ channel blockers

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• 作者：Swapnil Chavan ; Ahmed Abdelaziz…
• 关键词：Classification model ; hERG blockers ; Ikr ; KCNH2 ; k ; nearest neighbor (k ; NN) ; Toxicity
• 刊名：Journal of Computer-Aided Molecular Design
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：30
• 期：3
• 页码：229-236
• 全文大小：487 KB
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Physical Chemistry
  Computer Applications in Chemistry
  Animal Anatomy, Morphology and Histology
  
• 出版者：Springer Netherlands
• ISSN：1573-4951

文摘

A series of 172 molecular structures that block the hERG K+ channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.