Recent Advances in Ligand-Based Drug Design: Relevance and Utility of the Conformationally Sampled Pharmacophore Approach

Abstract

In the absence of three-dimensional (3D) structures of potential drug targets, ligand-based drug design is one of the popular approaches for drug discovery and lead optimization. 3D structureactivity relationships (3D QSAR) and pharmacophore modeling are the most important and widely used tools in ligand-based drug design that can provide crucial insights into the nature of the interactions between drug target and ligand molecule and provide predictive models suitable for lead compound optimization. This review article will briefly discuss the features and potential application of recent advances in ligand-based drug design, with emphasis on a detailed description of a novel 3D QSAR method based on the conformationally sample pharmacophore (CSP) approach (denoted CSP-SAR). In addition, data from a published study is used to compare the CSP-SAR approach to the Catalyst method, emphasizing the utility of the CSP approach for ligand-based model development.

Structural insights into mode of actions of novel natural Mycobacterium protein tyrosine phosphatase B inhibitors

“…It carefully excludes the group of variables with high internal correlation. It efficiently reduces the number of independent variables to be used in the QSAR model by removing all possible redundancy and limiting the variables with descriptor values to a smaller set of uncorrelated variables [13]. Various parameters were set for the execution of stepwise principle component regression analysis.…”

“…The procedure was repeated until all the compounds had once served as a test compound. The predictive ability of the model was then assessed using the cross validated r 2 and q 2 [13]. External validation was done by predicting the activities of the compounds of the test set which were not used for model generation.…”

Computational Methods in Drug Discovery

“…Information described can include properties such as molecular weight, geometry, volume, surface areas, ring content, rotatable bonds, interatomic distances, bond distances, atom types, planar and nonplanar systems, molecular walk counts, electronegativities, polarizabilities, symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, solvation properties, and many others (Cramer et al, 1988;Randic, 1995;Schuur et al, 1996;Bravi et al, 1997;Hemmer et al, 1999;Pearlman and Smith, 1999;Hong et al, 2008;Roberto Todeschini, 2010). These descriptors are generated through knowledge-based, graph-theoretical methods, molecularmechanical, or quantum-mechanical tools (Acharya et al, 2011;Marrero-Ponce et al, 2012) and are classified according to the "dimensionality" of the chemical representation from which they are computed (Ekins et al, 2007): 1D, scalar physicochemical properties such as molecular weight; 2D, molecular constitution-derived descriptors; 2.5D, molecular configuration-derived descriptors; 3D, molecular conformation-derived descriptors. These different levels of complexity, however, are overlapping with the more complex descriptors, often incorporating information from the simpler ones.…”

Molecular Docking and Structure-Based Drug Design Strategies

“…The unique chemical diversity available in these libraries represents the space occupied by ligands known to interact with a specific target. This type of information is used in ligand-based drug design (LBDD) methods [7]. Ligand-based virtual screening (LBVS), similarity searching, QSAR modeling and pharmacophore generation are some of the most useful LBDD methods [8].…”