%0 Journal Article
%T Quantitative Structure-Activity Relationship of Tricyclic Carbapenems: Application of Artificial Intelligence Methods for Bioactivity Prediction
%A Lebez
%A Mira
%A £¿olmajer
%A Tom
%A £¿upan
%A Jure
%J -
%D 2002
%X Sa£¿etak Resistance to antibiotics in bacterial population has widened the interest of Scientific community for development of novel therapeutic compounds. Penicillins and cephalosporins which share the ¦Â-lactam structural moiety form the most abundant group of antibiotics on the market. Their recently developed tricyclic analogues have shown remarkable bioactivity towards broad spectrum of bacterial species. In a series of 52 tricyclic carbapenems represented by the 180¡¯dimensional £¿spectrum-like£¿ representation we studied the structure-activity relationships by application of an artificial neural network. The molecular structure representation by spec-tral intensity values served as inputs into the counter-propagation artificial neural network (CP-ANN). SIMPLEX optimization was carried out to obtain the best ANN model and a genetic algorithm approach was subsequently used to simultaneously minimize the number of variables. Thus, a search for the substituents that predominantly influence the experimental bioactivity was performed. The constructed CP-ANN model yielded bioactivity values predictions with a correlation coefficient of 0.88, with their values extended over 4 orders of magnitude. The list of substituents selected by our automatic procedure can be compared with the data obtained by protein crystallography of the ¦Â-lactam inhibitors in complex with D,D-peptidase enzyme
%K QSAR
%K tricyclic carbapenem derivatives
%K antibiotic ac-tivity
%K articial neural networks
%K genetic algorithms
%U https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=188310