Abstract

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. © 2008 The Authors.