Prediction of selectivity of alpha1-adrenergic antagonists by counterpropagation neural network (CP-ANN).
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.
Eric
S
S
Department of Pharmaceutical Chemistry and Drug Analysis, Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia and Montenegro. seric@pharmacy.bg.ac.yu
Solmajer
T
T
Zupan
J
J
Novic
M
M
Oblak
M
M
Agbaba
D
D
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*
D000317:Adrenergic alpha-Antagonists; D018340:Receptors, Adrenergic, alpha-1