Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity.
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.
Pourbasheer
Eslam
E
Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
Riahi
Siavash
S
Ganjali
Mohammad Reza
MR
Norouzi
Parviz
P
D000465:Algorithms; D020128:Inhibitory Concentration 50; D016014:Linear Models; D024681:Potassium Channels, Calcium-Activated / Q000502:physiology*; D021281:Quantitative Structure-Activity Relationship
D024681:Potassium Channels, Calcium-Activated