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Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques by: Dr. Omar Deeb

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Field of Research: Pharmacology
Name of author and co-authors on the published work; Omar Deeb, Basheerulla Shaik, and Vijay K. Agrawal
Name of Journal or Book: Journal of Enzyme Inhibition and Medicinal Chemistry
Journal Impact Factor: 2.332
# of Pages, from : to:   670-676
Year:  2014
Journal volume:  29(5)
Publisher’s name and address: Taylor & Francis
Abstract of Published work:  
Quantitative Structure–Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models
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