title: Prediction of selectivity of alpha1-adrenergic antagonists by counterpropagation neural network (CP-ANN).
abstract: A quantitative structure-selectivity relationships of series of structurally diverse alpha1-adrenergic antagonists was performed by using counter-propagation neural network (CP-ANN). The theoretical molecular descriptors have been calculated and selected using CODESSA program. The results obtained for a highly non-congeneric set of molecules have confirmed the potential of use of CP-ANN approach in prediction of relative activity (selectivity) of alpha1-adrenergic antagonists.
authors.affiliation: Department of Pharmaceutical Chemistry and Drug Analysis, Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia and Montenegro. seric@pharmacy.bg.ac.yu
authors.forename: S
authors.initials: S
authors.lastname: Eric
authors.forename: T
authors.initials: T
authors.lastname: Solmajer
authors.forename: J
authors.initials: J
authors.lastname: Zupan
authors.forename: M
authors.initials: M
authors.lastname: Novic
authors.forename: M
authors.initials: M
authors.lastname: Oblak
authors.forename: D
authors.initials: D
authors.lastname: Agbaba
mesh_terms: D000317:Adrenergic alpha-Antagonists / Q000737:chemistry / Q000494:pharmacology*; D000465:Algorithms; D008954:Models, Biological; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D018340:Receptors, Adrenergic, alpha-1 / Q000187:drug effects / Q000378:metabolism*
chemical_list: D000317:Adrenergic alpha-Antagonists; D018340:Receptors, Adrenergic, alpha-1