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Cholesteryl ester transfer protein (CETP) is involved in trafficking lipoprotein particles and neutral lipids between HDL and LDL and therefore is considered a valid target for treating dyslipidemic conditions and complications. Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements for potent CETP inhibitors. Two pharmacophores emerged in the optimal QSAR equation (r(2)=0.800, n=96, F=72.1, r(2)(LOO) =0.775, r(2)(PRESS) against 22 external test inhibitors=0.707) suggesting the existence of at least two distinct binding modes accessible to ligands within CETP binding pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic (ROC) curve profiles. The validity of our modeling approach was experimentally established by the identification of several CETP inhibitory leads retrieved via in silico screening of the National Cancer Institute (NCI) list of compounds and an in house built database of drugs and agrochemicals. Two hits illustrated low micromolar IC(50) values: NSC 40331 (IC(50)=6.5 microM) and NSC 89508 (IC(50)=1.9 microM). Active hits were then used to guide synthetic exploration of a new series of CETP inhibitors., (Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.)

Homology modeling is becoming a valid method for obtaining three-dimensional coordinates for proteins. However, it is hard to judge the qualities of the resulting models warranting robust subsequent validations. In an attempt to evaluate the quality of Melanin-concentrating hormone 1 receptor (MCH1R) homology models, a number of homology structures were scanned for potential binding cavities. Subsequently, a group of 35 benzylpiperidines' MCH1R inhibitors were docked into each of the proposed binding sites via four different scoring functions. The docked structures were utilized to construct corresponding protein-aligned comparative molecular field analysis (CoMFA) models by employing probe-based (H(+), OH, CH(3)) energy grids and genetic partial least squares (G/PLS) statistical analysis. The docking-based alignment succeeded in accessing self-consistent CoMFA models upon employing JAIN scoring function in one of the proposed binding pockets in a particular homology model. Furthermore, a ligand-based pharmacophore model was developed for the same set of inhibitors and was found to agree with the successful docking configuration. Therefore, we proved that the overall procedure of docking, scoring, and CoMFA evaluation can be a useful tool to validate homology models, which can be of value for structure-based design, in-silico screening, and in understanding the structural basis of ligand binding to MCH1R.