Prediction of selectivity of alpha1-adrenergic antagonists by counterpropagation neural network (CP-ANN)

S Eric; T Solmajer; J Zupan; M Novic; M Oblak; D Agbaba

doi:10.1016/j.farmac.2003.12.009

Prediction of selectivity of alpha1-adrenergic antagonists by counterpropagation neural network (CP-ANN)

Farmaco. 2004 May;59(5):389-95. doi: 10.1016/j.farmac.2003.12.009.

Authors

S Eric¹, T Solmajer, J Zupan, M Novic, M Oblak, D Agbaba

Affiliation

¹ Department of Pharmaceutical Chemistry and Drug Analysis, Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia and Montenegro. [email protected]

PMID: 15120318
DOI: 10.1016/j.farmac.2003.12.009

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adrenergic alpha-Antagonists / chemistry
Adrenergic alpha-Antagonists / pharmacology*
Algorithms
Models, Biological
Neural Networks, Computer*
Quantitative Structure-Activity Relationship
Receptors, Adrenergic, alpha-1 / drug effects
Receptors, Adrenergic, alpha-1 / metabolism*

Substances

Adrenergic alpha-Antagonists
Receptors, Adrenergic, alpha-1