title: Quantitative structure-retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography.
abstract: Artificial neural network (ANN) is a learning system based on a computational technique which can simulate the neurological processing ability of the human brain. It was employed for building of the quantitative structure-retention relationships (QSRRs) model of antifungal agents-imidazoles or triazoles by structure. Computed molecular descriptors together with the percentage of acetonitrile in mobile phase (v/v) and buffer pH, being the most influential HPLC factors, were used as network inputs, giving the retention factor as model output. The multilayer perceptron network with a 9-5-1 topology was trained by using the back propagation algorithm. Good correlation between experimentally obtained data and ones predicted by using QSRR-ANN on previously unseen data sets indicates good predictive ability of the model.
authors.affiliation: University of Belgrade-Faculty of Pharmacy, Department of Drug Analysis, Vojvode Stepe 450, 11 152 Belgrade, Serbia.
authors.forename: Jelena
authors.initials: J
authors.lastname: Golubović
authors.forename: Ana
authors.initials: A
authors.lastname: Protić
authors.forename: Mira
authors.initials: M
authors.lastname: Zečević
authors.forename: Biljana
authors.initials: B
authors.lastname: Otašević
authors.forename: Marija
authors.initials: M
authors.lastname: Mikić
authors.forename: Ljiljana
authors.initials: L
authors.lastname: Živanović
mesh_terms: D000935:Antifungal Agents / Q000737:chemistry*; D001393:Azoles / Q000737:chemistry*; D002021:Buffers; D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D056148:Chromatography, Reverse-Phase / Q000379:methods*; D006863:Hydrogen-Ion Concentration; D016571:Neural Networks, Computer
chemical_list: D000935:Antifungal Agents; D001393:Azoles; D002021:Buffers