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Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction

Peter Nemeček, Tatiana Ďurčeková, Ján Mocák, and Karel Waisser

Department of Chemistry, Faculty of Natural Sciences, University of St. Cyril and Methodius, J. Herdu 2, SK-917 01 Trnava, Slovakia



Received: 10 December 2007  Revised: 9 June 2008  Accepted: 12 June 2008

Abstract: This study gives a quantitative structure-activity relationship (QSAR) correlation of the 72 N-benzylsalicylamide derivatives properties with their antimycobacterial activity. The antimycobacterial activity was measured as the minimal inhibition concentration (MIC) determined for four strains of mycobacterium (M. avium, M. kansasii, M. kansasii clin.-clinically isolated form, and M. tuberculosis) after 14 days and after 21 days of cultivation. The objective was to identify the factors most closely defining biological activity of N-benzylsalicylamides, in order to enable QSAR prediction of new derivatives with high antimycobacterial activity. Optimal properties for the QSAR analysis were selected from several physicochemical properties, including lipophilicity parameter log P, molecular mass M, molar refraction MR, NMR chemical shifts, polarizability, etc. Many of the considered properties are different from those typically used in traditional QSAR. Selection of the most important properties was performed by one-way Analysis of Variance (ANOVA) and correlation analysis using the significance coefficients and the correlation coefficients, respectively. The chosen variables were further used in artificial neural networks (ANN) for predicting biological activity in the form of-log(MIC).

Keywords: N-benzylsalicylamide - biological activity - mycobacterium - QSAR - analysis of variance - artificial neural networks

Full paper is available at

DOI: 10.2478/s11696-008-0089-9


Chemical Papers 63 (1) 84–91 (2009)

Wednesday, January 20, 2021

SCImago Journal Rank 2019
XXVIII. International Conference on Coordination and Bioinorganic Chemistry
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