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Please use this identifier to cite or link to this item: http://dspace.utalca.cl/handle/1950/4804

Title: QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-alpha-phenylsulfonylacetamide derivatives
Authors: Fernandez, M.
Caballero, J.
Keywords: MMP inhibitors; Bayesian-regularized genetic neural networks; QSAR analysis; 2D autocorrelation space
Issue Date: 2007
Publisher: Elsevier Ltd
Citation: Bioorganic & Medicinal Chemistry 15(18): 6298-6310
Abstract: The main molecular features which determine the selectivity of a set of 80 N-hydroxy-α-phenylsulfonylacetamide derivatives (HPSAs) in the inhibition of three matrix metalloproteinases (MMP-1, MMP-9, and MMP-13) have been identified by using linear and nonlinear predictive models. The molecular information has been encoded in 2D autocorrelation descriptors, obtained from different weighting schemes. The linear models were built by multiple linear regression (MLR) combined with genetic algorithm (GA), and a robust QSAR mapping paradigm. The Bayesian-regularized genetic neural network (BRGNN) was employed for nonlinear modeling. In such approaches each model could have its own set of input variables. All models were predictive according to internal and external validation experiments; but the best results correspond to nonlinear ones. The 2D autocorrelation space brings different descriptors for each MMP inhibition, and suggests the atomic properties relevant for the inhibitors to interact with each MMP active site. On the basis of the current results, the reported models have the potential to discover new potent and selective inhibitors and bring useful molecular information about the ligand specificity for MMP and subsites.
Description: Caballero, J. Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile.
URI: http://dspace.utalca.cl/handle/1950/4804
ISSN: 0968-0896
Appears in Collections:Artículos en publicaciones ISI - Universidad de Talca

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