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- Title
A k-nearest neighbor classification of hERG K channel blockers.
- Authors
Chavan, Swapnil; Abdelaziz, Ahmed; Wiklander, Jesper; Nicholls, Ian
- Abstract
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 Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.
- Subjects
DATA modeling; INFORMATION modeling; DATA analysis; MOLECULAR structure; COMPUTER simulation
- Publication
Journal of Computer-Aided Molecular Design, 2016, Vol 30, Issue 3, p229
- ISSN
0920-654X
- Publication type
Academic Journal
- DOI
10.1007/s10822-016-9898-z