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Proteochemometric Modeling of the Inhibition Complexes of Matrix Metalloproteinases with N ‐Hydroxy‐2‐[(Phenylsulfonyl)Amino]Acetamide Derivatives Using Topological Autocorrelation Interaction Matrix and Model Ensemble Averaging
Author(s) -
Fernández Michael,
Fernández Leyden,
Caballero Julio,
Abreu José Ignacio,
Reyes Grethel
Publication year - 2008
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2008.00675.x
Subject(s) - autocorrelation , acetamide , quantitative structure–activity relationship , mathematics , topology (electrical circuits) , biological system , bioinformatics , chemistry , stereochemistry , combinatorics , biology , statistics , organic chemistry
A target‐ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC 50 ) toward matrix metalloproteinases (MMP‐1, MMP‐2, MMP‐3, MMP‐9, and MMP‐13) of N ‐hydroxy‐2‐[(phenylsulfonyl)amino]acetamide derivatives. Target and ligand structural information was encoded in the Topological Autocorrelation Interaction matrix calculated from 2D topological representation of inhibitors and protein sequences. The relevant Topological Autocorrelation Interaction descriptors were selected by genetic algorithm‐based multilinear regression analysis and Bayesian‐regularized genetic neural network approaches. A model ensemble strategy was employed for achieving robust and reliable linear and non‐linear predictors having nine topological autocorrelation interaction descriptors with square correlation coefficients of ensemble test‐set fitting ( R 2 test ) about 0.80 and 0.87, respectively. Electrostatic and hydrophobicity/hydrophilicity properties were the most relevant on the optimum models. In addition, the distribution of the inhibition complexes on a self‐organized map depicted target dependence rather than an inhibitor similarity pattern.