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 S1 ′ and S2 ′ subsites. © 2007 Elsevier Ltd. All rights reserved.