Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives

Title: Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives

Volume: 7 Issue: 4

Author(s): Adam Bucinski, Michal Jan Markuszewski, Wlodzimierz Wiktorowicz, Jerzy Krysinski and Roman Kaliszan

Affiliation:

Keywords: anns, antibacterial activity, imidazole derivatives, sensitivity analysis

Abstract: Artificial neural networks (ANNs) have been applied for the quantitative structure-activity relationships (QSAR) studies of antibacterial activity against Escherichia coli, Serratia marcescens, Proteus vulgaris, Klebsiella pneumoniae and Pseudomonas aeruginosa of a large series of new imidazole derivatives. Antibacterial activity against individual bacteria, expressed as logarithm of reciprocal of the minimal inhibitory concentrations, log 1 / MIC, has been related to a number of physicochemical and structural parameters of the imidazole derivatives investigated. Molecular descriptors of agents were obtained by quantum-chemical calculations combined with molecular modelling and from respective structure fragment reference data (e.g., log P). A high correlation resulted between the predicted from ANN model antibacterial activity, log 1 / MICANN, and that from biological experiments, log 1 / MICexp, both for the data used in learning and in the testing sets of imidazoles. Correlation coefficient, R, depending on the type of bacteria and structural subset of analysed imidazole compounds, varies from 0.875 to 0.969. The applicability of ANNs has been demonstrated for the prediction of pharmacological potency of new imidazole derivatives based on their structural descriptors generated exclusively by calculation chemistry.

Cite this article as:

Bucinski Adam, Markuszewski Jan Michal, Wiktorowicz Wlodzimierz, Krysinski Jerzy and Kaliszan Roman, Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives, Combinatorial Chemistry & High Throughput Screening 2004; 7 (4) . https://dx.doi.org/10.2174/1386207043328652