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), 2024. Published by Science Publishing Group |
QSAR, Artificial Neural Networks, Candida Albicans, Drug Design
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APA Style
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
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
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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Download@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}
}
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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