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A linear quantitative structure-activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently(More)
Quantitative structure activity relationship (QSAR) of the melanocortin-4 receptor (MC4R) binding affinities (K(i)) of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides of piperazinecyclohexanes was studied. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors that resulted in the(More)
Pioglitazone is a medicine of thiazolidinedione (TZD) class with hypoglycemic (antihyperglycemic, antidiabetic) action. Pioglitazone binding to human serum albumin (HSA) was investigated at different temperatures (290, 300 and 310 K) by fluorescence spectroscopic method. Molecular docking study was also carried out besides the experiments. Experimental(More)
Computational studies have been carried out at the DFT-B3LYP/6-31G(d) level of theory on the structural and spectroscopic properties of 2-(1-oxo-1 H-inden-3-yl)-2H-indene-1,3-dione (OID). Experimental studies were conducted on these parameters, including X-ray, FT-IR and 13C NMR spectroscopy. The optimized geometries of OID and its bonding characteristics(More)
Precise detection of 3-hydroxybutyrate (HB) in biological samples is of great importance for management of diabetic patients. In this study, an HB biosensor based on single-walled carbon nanotubes (SWCNTs)-modified screen-printed electrode (SPE) was developed to determine the concentration of HB in serum. The specific detecting enzyme, HB dehydrogenase, was(More)
The quantitative structure-retention relationship (QSRR) of the essential oil components against the gas chromatography retention index (RI) was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions [e.g. the multiple linear(More)
Multiple linear regressions (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship (QSAR) models of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as chemical, topological, geometrical,(More)
Quantitative structure-activity relationship of the 2-(1-propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide as a potent inhibitor of poly(ADP-ribose) polymerase for cancer treatment was studied. A suitable set of molecular descriptors was calculated and the genetic algorithm was employed to select those descriptors that resulted in the best fitted models.(More)
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(More)
The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitative structure activity relationship models to predict the antiviral activity of 4-alkylamino-6-(2-hydroxyethyl)-2-methylthiopyrimidines. The genetic algorithm was employed to select the variables that resulted in the best-fitted models. A(More)