Quantitative structure-activity relationship (QSAR) studies of quinolone antibacterials against M-fortuitum and M-smegmatis using theoretical molecular descriptors
Metadata of CSIR Papers
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| Title |
Quantitative structure-activity relationship (QSAR) studies of quinolone antibacterials against M-fortuitum and M-smegmatis using theoretical molecular descriptors
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| Creator |
Bagchi, MC
Mills, D Basak, SC |
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| Subject |
Biochemistry & Molecular Biology; Biophysics; Chemistry, Multidisciplinary; Computer Science, Interdisciplinary Applications
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| Description |
The incidence of tuberculosis infections that are resistant to conventional drug therapy has risen steadily in the last decade. Several of the quinolone antibacterials have been examined as inhibitors of M. tuberculosis infection as well as other mycobacterial infections. However, not much has been done to examine specific structure-activity relationships of the quinolone antibacterials against mycobacteria. The present paper describes quantitative structure-activity relationship modeling for a series of antimycobacterial compounds. Most of the antimycobacterial compounds do not have sufficient physicochemical data, and thus predictive methods based on experimental data are of limited use in this situation. Hence, there is a need for the development of quantitative structure-activity relationship (QSAR) models utilizing theoretical molecular descriptors that can be calculated directly from molecular structures. Descriptors associated with chemical structures of N-1 and C-7 substituted quinolone derivatives as well as 8-substituted quinolone derivatives with good antimycobacterial activities against M. fortuitum and M. smegmatis have been evaluated. Ridge regression (RR), Principal component regression (PCR), and partial least squares (PLS) regression were used, comparatively, to develop predictive models for antibacterial activity, based on the activities of the above compounds. The independent variables include topostructural, topochemical and 3-D geometrical indices, which were used in a hierarchical fashion in the model-development process. The predictive ability of the models was assessed by the cross-validated R-2. Comparison of the relative effectiveness of the various classes of molecular descriptors in the regression models shows that the easily calculable topological indices explain most of the variance in the data.
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| Publisher |
SPRINGERNEW YORK233 SPRING STREET, NEW YORK, NY 10013 USA
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| Date |
2011-09-20T12:12:42Z
2011-09-20T12:12:42Z 2007 |
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| Type |
Article
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| Identifier |
JOURNAL OF MOLECULAR MODELING
1610-2940 http://hdl.handle.net/123456789/14390 |
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| Language |
English
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