Prediction of glucagon receptor antagonist activities of some substituted imidazoles using combined radial basis function neural network and density functional theory

Abstract

QSAR study of human glucagon receptor (HGR) ligands has been made with the help of quantum descriptors, such as energy of HOMO, energy of LUMO, softness, hardness using combination of principal component analysis, and radial basis function artificial neural network (ANN). Quantum descriptors have been calculated via the DFT-B3LYP method, with the basis set 6-311G. The developed neural network QSAR model outperformed the principal component regression model in both fitting and predictive abilities. ANN analysis indicated that the estimated activities were in total agreement with the experimentally observed values (R2 = 0.869, RMSD = 0.186; predictive Q2 = 0.732, RMSEcv = 0.346). The developed models were further examined by means of an external prediction set. The modeling study also reflected the important role of quantum properties of molecules when they interact with the target, HGR. The developed neural network model is expected to be useful in the rational design of new chemical entities as ligands of HGR and also for directing the synthesis of potent molecules in the future.