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Artificial neural networks for prediction of antibacterial activity in series of imidazole derivatives.

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/MIC(ANN), and that from biological experiments, log 1/MIC(exp), 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.

journal_name

Comb Chem High Throughput Screen

journal_title

Combinatorial chemistry & high throughput screening

authors

Bucinski A,Markuszewski MJ,Wiktorowicz W,Krysinski J,Kaliszan R

doi

10.2174/1386207043328652

subject

Has Abstract

pub_date

2004-06-01 00:00:00

pages

327-36

issue

4

eissn

1386-2073

issn

1875-5402

journal_volume

7

pub_type

杂志文章

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