2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks.
Negative inotropic potency of 60 benzothiazepine-like calcium entry blockers (CEBs), Diltiazem analogs, was successfully modeled using Bayesian-regularized genetic neural networks (BRGNNs) and 2D autocorrelation vectors. This approach yielded reliable and robust models whilst by means of a linear genetic algorithm (GA) search routine no multilinear regression model was found describing more than 50% of the training set. On the contrary, the optimum neural network predictor with five inputs described about 84% and 65% variances of 50 randomly selected training and test sets. Autocorrelation vectors in the nonlinear model contained information regarding 2D spatial distributions on the CEB structure of van der Waals volumes, electronegativities, and polarizabilities. However, a sensitivity analysis of the network inputs pointed out to the electronegativity and polarizability 2D topological distributions at substructural fragments of sizes 3 and 4 as the most relevant features governing the nonlinear modeling of the negative inotropic potency.
Caballero
Julio
J
Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, 44740 Matanzas, Cuba.
Garriga
Miguel
M
Fernández
Michael
M
D001499:Bayes Theorem; D002121:Calcium Channel Blockers / Q000737:chemistry* / Q000494:pharmacology*; D003198:Computer Simulation; D004110:Diltiazem / Q000031:analogs & derivatives / Q000737:chemistry / Q000494:pharmacology; D015195:Drug Design; D008954:Models, Biological; D009200:Myocardial Contraction / Q000187:drug effects*; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
D002121:Calcium Channel Blockers; D004110:Diltiazem