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Ensembles of Bayesian‐regularized Genetic Neural Networks for Modeling of Acetylcholinesterase Inhibition by Huprines
Author(s) -
Fernández Michael,
Caballero Julio
Publication year - 2006
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2006.00435.x
Subject(s) - acetylcholinesterase , bayesian probability , artificial neural network , artificial intelligence , computational biology , computer science , machine learning , neuroscience , biology , biochemistry , enzyme
Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian‐regularized Genetic Neural Networks. In the Bayesian‐regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three‐dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R 2 ‐values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self‐organizing map. The analysis of the self‐organizing map zones allows establishing the main structural features differentiated by our vectorial space.