Modeling of the Inhibition Constant (<i>K</i>_i) of Some Cruzain Ketone‐Based Inhibitors Using 2D Spatial Autocorrelation Vectors and Data‐Diverse Ensembles of Bayesian‐Regularized Genetic Neural Networks

Abstract

Abstract The inhibition constant ( K i ) 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.