Comparative QSAR modeling of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas

The present QSAR study attempts to explore the structural and physicochemical requirements of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas for CCR5 binding affinity using linear free energy-related (LFER) model of Hansch. QSAR models have been developed using electronic (Hammett σ), hydrophobicity (π), and steric (molar refractivity and STERIMOL L, B1, and B5) parameters of phenyl ring substituents of the compounds along with appropriate dummy variables. Whole molecular descriptor like partition coefficient (log Pcalcd) was also tried as an additional descriptor. Statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing step (FA-MLR), partial least squares with factor analysis as the preprocessing step (FA-PLS), principal component regression analysis (PCRA), multiple linear regression with genetic function approximation (GFA-MLR), and genetic partial least squares (G/PLS) were applied to identify the structural and physicochemical requirements for the CCR5 binding affinity. The generated equations were statistically validated using leave-one-out technique. The quality of equations obtained from stepwise regression, FA-MLR, FA-PLS, and PCRA is of acceptable statistical range (explained variance ranging from 71.9% to 80.4%, while predicted variance ranging from 67.4% to 77.0%). The GFA-derived models show high intercorrelation among predictor variables used in the equations while the G/PLS model shows lowest statistical quality among all types of models. The best models were also subjected to leave-25%-out crossvalidation.

Graphical abstractCCR5 binding affinity data of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas have been subjected to quantitative structure-activity relationship (QSAR) study by linear free energy-related (LFER) model of Hansch using stepwise regression, FA-MLR, FA-PLS, PCRA, GFA-MLR, and G/PLS techniques.Figure optionsDownload full-size imageDownload as PowerPoint slide