Molecular Diversity

, Volume 15, Issue 2, pp 417–426 | Cite as

Quantitative structure–activity relationship study of antitubercular fluoroquinolones

  • Nikola Minovski
  • Marjan Vračko
  • Tom Šolmajer
Full-length paper


Quantitative structure–activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (μg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure–activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.


Tuberculosis Fluoroquinolones DNA gyrase QSAR CP ANN 



Adenosine triphosphate


Minimal inhibitory concentration


Structure–activity relationship


Quantitative structure–activity relationship


Comprehensive descriptors for structural and statistical analysis


Multiple linear regression


Neural networks


Artificial neural networks


Counter propagation artificial neural networks


Kohonen artificial neural networks


Self organizing maps


Cross validation leave-one-out


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Supplementary material

11030_2010_9238_MOESM1_ESM.doc (354 kb)
ESM 1 (DOC 354 kb)
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11030_2010_9238_MOESM4_ESM.xls (28 kb)
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11030_2010_9238_MOESM5_ESM.xls (19 kb)
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11030_2010_9238_MOESM6_ESM.doc (120 kb)
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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Nikola Minovski
    • 1
  • Marjan Vračko
    • 1
  • Tom Šolmajer
    • 1
  1. 1.National Institute of ChemistryLjubljanaSlovenia

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