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Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction
Nemeček, Peter, Ďurčeková, Tatiana, Mocák, Ján and Waisser, Karel Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction Chemical Papers, Vol.63, No. 1, 2009, 84-91
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Document type:
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Článok z časopisu / Journal Article |
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Collection:
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Chemical papers
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| Author(s) |
Nemeček, Peter
Ďurčeková, Tatiana
Mocák, Ján
Waisser, Karel
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| Title |
Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction
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| Journal name |
Chemical Papers
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| Publication date |
2009
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| Year available |
2009
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| Volume number |
63
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| Issue number |
1
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| ISSN |
0366-6352
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| Start page |
84
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| End page |
91
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| Place of publication |
Poland
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| Publisher |
Versita
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| Collection year |
2009
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| Language |
english
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| Subject |
250000 Chemical Sciences
250400 Analytical Chemistry
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| Abstract/Summary |
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).
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