Docking and receptor-based QSAR approaches for modeling of CETP inhibitors

Abstract

Plasma cholesteryl ester transfer protein (CETP) inhibitors are currently considered as potential drugs for treating high low-density lipoprotein cholesterol. In this work, we developed a receptor-based quantitative structure–activity relationship (QSAR) models based on a series of tetrahydronaphthyridines derivatives to support the design of new CETP inhibitors. Multivariate adaptive regression spline and adaptive neuro-fuzzy inference system were employed to select the best subset of descriptors and mapping tool. The obtained QSAR model indicates that the inhibitory activity can be described by relative negative charge, Moriguchi octanol–water partition coefficient, topological electronic indices, steric interaction, and hydrogen bonding energies between the receptor and the inhibitors. In addition, the docking analysis showed that the interaction of the inhibitors with residues of the ARG201 and ASP460 residues plays an important role in the activities of the inhibitors. The results of validation and applicability domain techniques show that the models exhibited optimum stability and good predictive power which can be used in prediction of activity of new CETP inhibitors.