title: Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds.
abstract: Inhibitory activity against aldose reductase enzyme of flavonoid derivatives were modelled using 11 kinds of molecular descriptors from Dragon software. Model with four Galvez Charge Indices described 67% of data variance and overtaken other models using the same number of variables. Galvez indices showed to contain important information on the relationship between the inhibitor structures and its activity by describing the molecular topology and charge transfer through the molecule. In addition, artificial neural networks were trained using charge indices from the linear models but the obtaining networks overfitted the data having low predictive power.
authors.affiliation: Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, 44740 Matanzas, Cuba.
authors.forename: Michael
authors.initials: M
authors.lastname: Fernández
authors.forename: Julio
authors.initials: J
authors.lastname: Caballero
authors.forename: Aliuska Morales
authors.initials: AM
authors.lastname: Helguera
authors.forename: Eduardo A
authors.initials: EA
authors.lastname: Castro
authors.forename: Maykel Pérez
authors.initials: MP
authors.lastname: González
mesh_terms: D000449:Aldehyde Reductase / Q000037:antagonists & inhibitors* / Q000737:chemistry; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D005419:Flavonoids / Q000737:chemistry* / Q000494:pharmacology*; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D005419:Flavonoids; D000449:Aldehyde Reductase