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• W1982178440 startingPage "5023" @default.
• W1982178440 abstract "The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure–activity relationship (QSAR) for BK-channel activators. The data set was divided into 57 molecules of training and 14 molecules of test sets. A large number of descriptors were calculated and genetic algorithm (GA) was used to select variables that resulted in the best-fitted for models. A comparison between the obtained results using SVM with those of multi-parameter linear regression (MLR) revealed that SVM model was much better than MLR model. The improvements are due to the fact that the activity of the compounds demonstrates non-linear correlations with the selected descriptors. Also distances between Oxygen and Chlorine atoms, the mass, the van der Waals volume, the electronegativity, and the polarizability of the molecules are the main independent factors contributing to the BK-channels activity of the studied compounds." @default.
• W1982178440 created "2016-06-24" @default.
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• W1982178440 date "2009-12-01" @default.
• W1982178440 modified "2023-10-16" @default.
• W1982178440 title "Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity" @default.
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• W1982178440 doi "https://doi.org/10.1016/j.ejmech.2009.09.006" @default.
• W1982178440 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19837488" @default.
• W1982178440 hasPublicationYear "2009" @default.
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