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QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives

Received: 29 April 2014     Accepted: 14 May 2014     Published: 30 May 2014
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Abstract

QSAR analysis of a set of previously synthesized azole derivatives tested for growth inhibitory activity against Candida albicans was performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was used. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2 = 0.77 - 0.79 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.70 - 0.80 for regressions. Biological testing of compounds was performed by disco-diffusion method on solid medium culture versus strain C. albicans ATCC 10231 M885. Most of compounds demonstrated high antifungal activity. Five synthesized compounds also showed activity against clinical isolate strain of C. albicans received from a biological material and resistant to fluconazole.

Published in Computational Biology and Bioinformatics (Volume 2, Issue 2)
DOI 10.11648/j.cbb.20140202.12
Page(s) 25-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

QSAR, Artificial Neural Networks, Candida Albicans, Drug Design

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Cite This Article
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    Vasyl Kovalishyn, Iryna Kopernyk, Svitlana Chumachenko, Oleg Shablykin, Kostyantyn Kondratyuk, et al. (2014). QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives. Computational Biology and Bioinformatics, 2(2), 25-32. https://doi.org/10.11648/j.cbb.20140202.12

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    ACS Style

    Vasyl Kovalishyn; Iryna Kopernyk; Svitlana Chumachenko; Oleg Shablykin; Kostyantyn Kondratyuk, et al. QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives. Comput. Biol. Bioinform. 2014, 2(2), 25-32. doi: 10.11648/j.cbb.20140202.12

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    AMA Style

    Vasyl Kovalishyn, Iryna Kopernyk, Svitlana Chumachenko, Oleg Shablykin, Kostyantyn Kondratyuk, et al. QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives. Comput Biol Bioinform. 2014;2(2):25-32. doi: 10.11648/j.cbb.20140202.12

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  • @article{10.11648/j.cbb.20140202.12,
      author = {Vasyl Kovalishyn and Iryna Kopernyk and Svitlana Chumachenko and Oleg Shablykin and Kostyantyn Kondratyuk and Stepan Pil’o and Volodymyr Prokopenko and Volodymyr Brovarets and Larysa Metelytsia},
      title = {QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives},
      journal = {Computational Biology and Bioinformatics},
      volume = {2},
      number = {2},
      pages = {25-32},
      doi = {10.11648/j.cbb.20140202.12},
      url = {https://doi.org/10.11648/j.cbb.20140202.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20140202.12},
      abstract = {QSAR analysis of a set of previously synthesized azole derivatives tested for growth inhibitory activity against Candida albicans was performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was used. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2 = 0.77 - 0.79 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.70 - 0.80 for regressions. Biological testing of compounds was performed by disco-diffusion method on solid medium culture versus strain C. albicans ATCC 10231 M885. Most of compounds demonstrated high antifungal activity. Five synthesized compounds also showed activity against clinical isolate strain of C. albicans received from a biological material and resistant to fluconazole.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives
    AU  - Vasyl Kovalishyn
    AU  - Iryna Kopernyk
    AU  - Svitlana Chumachenko
    AU  - Oleg Shablykin
    AU  - Kostyantyn Kondratyuk
    AU  - Stepan Pil’o
    AU  - Volodymyr Prokopenko
    AU  - Volodymyr Brovarets
    AU  - Larysa Metelytsia
    Y1  - 2014/05/30
    PY  - 2014
    N1  - https://doi.org/10.11648/j.cbb.20140202.12
    DO  - 10.11648/j.cbb.20140202.12
    T2  - Computational Biology and Bioinformatics
    JF  - Computational Biology and Bioinformatics
    JO  - Computational Biology and Bioinformatics
    SP  - 25
    EP  - 32
    PB  - Science Publishing Group
    SN  - 2330-8281
    UR  - https://doi.org/10.11648/j.cbb.20140202.12
    AB  - QSAR analysis of a set of previously synthesized azole derivatives tested for growth inhibitory activity against Candida albicans was performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was used. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2 = 0.77 - 0.79 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.70 - 0.80 for regressions. Biological testing of compounds was performed by disco-diffusion method on solid medium culture versus strain C. albicans ATCC 10231 M885. Most of compounds demonstrated high antifungal activity. Five synthesized compounds also showed activity against clinical isolate strain of C. albicans received from a biological material and resistant to fluconazole.
    VL  - 2
    IS  - 2
    ER  - 

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Author Information
  • Department of medical and biological researches, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department of medical and biological researches, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department for Chemistry of Bioactive Nitrogen-Containing Heterocyclic Compounds, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department for Chemistry of Bioactive Nitrogen-Containing Heterocyclic Compounds, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department for Chemistry of Bioactive Nitrogen-Containing Heterocyclic Compounds, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department for Chemistry of Bioactive Nitrogen-Containing Heterocyclic Compounds, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department for Chemistry of Bioactive Nitrogen-Containing Heterocyclic Compounds, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department for Chemistry of Bioactive Nitrogen-Containing Heterocyclic Compounds, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

  • Department of medical and biological researches, Institute of Bioorganic Chemistry and Petrochemistry, NAS of Ukraine, Kyiv, Ukraine

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