Inhibition activity modeling of CINPA1 analogs as novel inverse agonists of constitutive androstane receptor

In biological research, increasing the effectiveness of anticancer drugs and attenuating multi drug resistance are of prime importance. In this study, a combination of docking-quantitative structure activity relationship (QSAR) methods is implemented for modeling and predicting inhibition activity of CINPA1 analogs as novel inverse agonists of constitutive androstane receptor (CAR). We also proposed novel agents for inhibiting the CAR activity considering the QSAR results. The optimal ligands conformation as well as their position and orientation within active site were obtained from docking and were used for calculating molecular descriptors and investigation of different interactions in CAR ligand binding domain.The data set containing 50 molecules with known pIC50 was divided into training and test sets, each including 40 and 10 molecules, respectively. Binary version of gravitational search algorithm (GSA) and bayesian regularization based neural networks (BRANN) were applied to develop a predictive model using the most informative descriptors regarding the ligand binding positions. This analysis revealed that hydrophobic interactions, number of nitrogen atoms and cation-π interactions play important roles in the CAR inhibition activity of the agents.The results of external validation and internal cross-validation tests in conjunction with Y-randomization confirm the predictive ability, robustness and effectiveness of the generated model. This study shows that a combination of molecular docking and QSAR modeling are effective tool for characterization and synthesis of the potent inhibitors.