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QSAR Approaches (qsar + approach)
Selected AbstractsMolecular Modeling and Receptor-Dependent (RD) 3D-QSAR Approach to a Set of Antituberculosis DerivativesMOLECULAR INFORMATICS, Issue 11-12 2009Fernanda, Kerly, Mesquita Pasqualoto Abstract In this study, receptor-dependent (RD) 3D-QSAR models were built for a set of thirty-seven isoniazid derivatives bound to the enoyl-acp reductase from M. tuberculosis, called InhA (PDB entry code 1zid). Ligand-receptor (L-R) molecular dynamics (MD) simulations [500,000 steps; the step size was 0.001,ps (1,fs)] were carried out at 310,K (biological assay temperature). The hypothesized active conformations resulting from a previously reported receptor-independent (IR) 4D-QSAR analysis were used as the molecular geometries of each ligand in this structure-based L-R binding research. The dependent variable is the reported MIC values against M. tuberculosis var. bovis. The independent variables (descriptors) are energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model. Genetic function approximation (GFA) formalism and partial least squares (PLS) regression were employed as the fitting functions to develop 3D-QSAR models. The bound ligand solvation energy, the sum of electrostatic and hydrogen bonding energies of the unbound ligand, the bending energy of the unbound ligand, the electrostatic intermolecular L-R energy, and the change in hydrogen bonding energy upon binding were found as important energy contributions to the binding process. The 3D-QSAR model at 310,K has good internal and external predictability and may be regarded as representative of the binding process of ligands to InhA. [source] QSAR Modeling of a Set of Pyrazinoate Esters as Antituberculosis ProdrugsARCHIV DER PHARMAZIE, Issue 2 2010Joćo P. S. Fernandes Abstract Tuberculosis is an infection caused mainly by Mycobacterium tuberculosis. A first-line antimycobacterial drug is pyrazinamide (PZA), which acts partially as a prodrug activated by a pyrazinamidase releasing the active agent, pyrazinoic acid (POA). As pyrazinoic acid presents some difficulty to cross the mycobacterial cell wall, and also the pyrazinamide-resistant strains do not express the pyrazinamidase, a set of pyrazinoic acid esters have been evaluated as antimycobacterial agents. In this work, a QSAR approach was applied to a set of forty-three pyrazinoates against M. tuberculosis ATCC 27294, using genetic algorithm function and partial least squares regression (WOLF 5.5 program). The independent variables selected were the Balaban index (J), calculated n -octanol/water partition coefficient (ClogP), van-der-Waals surface area, dipole moment, and stretching-energy contribution. The final QSAR model (N = 32, r2 = 0.68, q2 = 0.59, LOF = 0.25, and LSE = 0.19) was fully validated employing leave- N -out cross-validation and y -scrambling techniques. The test set (N = 11) presented an external prediction power of 73%. In conclusion, the QSAR model generated can be used as a valuable tool to optimize the activity of future pyrazinoic acid esters in the designing of new antituberculosis agents. [source] Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicologyENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003Roger Perkins Abstract Quantitative structure,activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure,activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance. [source] Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codificationJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 15 2008Juan A. Castillo-Garit Abstract The recently introduced non-stochastic and stochastic bond-based linear indices are been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These improved modified descriptors are applied to several well-known data sets to validate each one of them. Particularly, Cramer's steroid data set has become a benchmark for the assessment of novel quantitative structure activity relationship methods. This data set has been used by several researchers using 3D-QSAR approaches such as Comparative Molecular Field Analysis, Molecular Quantum Similarity Measures, Comparative Molecular Moment Analysis, E-state, Mapping Property Distributions of Molecular Surfaces, and so on. For that reason, it is selected by us for the sake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design we model the angiotensin-converting enzyme inhibitory activity of perindoprilate's ,-stereoisomers combinatorial library, as well as codify information related to a pharmacological property highly dependent on the molecular symmetry of a set of seven pairs of chiral N -alkylated 3-(3-hydroxyphenyl)-piperidines that bind ,-receptors. The validation of this method is achieved by comparison with earlier publications applied to the same data sets. The non-stochastic and stochastic bond-based 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [source] | ||||||||||