Abstract

The inhibition constant (Ki) of a set of 46 ketone-based cruzain inhibitors against cysteine protease cruzain was successfully modeled by means of data-diverse ensembles of Bayesian-regularized genetic neural networks. 2D spatial autocorrelation vectors were used for encoding structural information yielding a nonlinear model describing about 90 and 75% of ensemble training and test set variances, respectively. From the results of a ranking analysis of the neural network inputs, it was derived that atomic van der Waals volume distributions at topological lags 3, 5, and 6 in the 2D topological structure of the inhibitors have a high nonlinear influence on the inhibition constants. Furthermore, optimum subset of autocorrelation vectors well mapped the studied compounds according to their inhibition constant values in a Kohonen self-organizing map. © 2007 Wiley-VCH Verlag GmbH & Co. KGaA.