Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines

Chemical Biology and Drug Design

Volumn 68, Issue 4, 2006, Pages 201-212

  Fernández, Michael ^a   Caballero, Julio ^a  

^a UNIVERSITY OF MATANZAS   (Cuba)

Author keywords

Acetylcholinesterase inhibitors; Bayesian regularized Genetic Neural Networks; Huprines; Neural network ensemble; Quantitative structure activity relationship; Self organizing maps

Indexed keywords

ACETYLCHOLINESTERASE; CHOLINESTERASE INHIBITOR; HUPRINE DERIVATIVE; UNCLASSIFIED DRUG;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; DATA ANALYSIS; GENETIC ALGORITHM; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL; RELIABILITY; STATISTICAL ANALYSIS; STRUCTURE ACTIVITY RELATION; VALIDATION STUDY;

ACETYLCHOLINESTERASE; ALGORITHMS; ALKALOIDS; ANIMALS; BAYES THEOREM; CATTLE; CHOLINESTERASE INHIBITORS; COMPUTER SIMULATION; DRUG DESIGN; MODELS, BIOLOGICAL; MOLECULAR STRUCTURE; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SESQUITERPENES; TACRINE;

EID: 33751032263     PISSN: 17470277     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1747-0285.2006.00435.x     Document Type: Article

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