Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans

2018
Authors
Kovacević, StrahinjaKaradzić, Milica
Podunavac-Kuzmanović, Sanja
Jevrić, Lidija
Ivanović, Evica

Vojnović, Matilda
Article (Published version)
Metadata
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The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based on their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans. The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered ...to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics.
Keywords:
Artificial neural networks / Antifungal activity / Molecular topology / Electrostatic descriptors / QSAR / Sum of Ranking DifferencesSource:
Acta Chimica Slovenica, 2018, 65, 3, 483-491Publisher:
- Slovensko Kemijsko Drustvo, Ljubljana
Funding / projects:
- Sustainable and green chemistry approach for environmental friendly analytical methods and energy storage (RS-172012)
DOI: 10.17344/acsi.2017.3532
ISSN: 1318-0207
WoS: 000444705500001
Scopus: 2-s2.0-85061105476
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Poljoprivredni fakultetTY - JOUR AU - Kovacević, Strahinja AU - Karadzić, Milica AU - Podunavac-Kuzmanović, Sanja AU - Jevrić, Lidija AU - Ivanović, Evica AU - Vojnović, Matilda PY - 2018 UR - http://aspace.agrif.bg.ac.rs/handle/123456789/4628 AB - The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based on their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans. The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics. PB - Slovensko Kemijsko Drustvo, Ljubljana T2 - Acta Chimica Slovenica T1 - Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans EP - 491 IS - 3 SP - 483 VL - 65 DO - 10.17344/acsi.2017.3532 ER -
@article{ author = "Kovacević, Strahinja and Karadzić, Milica and Podunavac-Kuzmanović, Sanja and Jevrić, Lidija and Ivanović, Evica and Vojnović, Matilda", year = "2018", abstract = "The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based on their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans. The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics.", publisher = "Slovensko Kemijsko Drustvo, Ljubljana", journal = "Acta Chimica Slovenica", title = "Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans", pages = "491-483", number = "3", volume = "65", doi = "10.17344/acsi.2017.3532" }
Kovacević, S., Karadzić, M., Podunavac-Kuzmanović, S., Jevrić, L., Ivanović, E.,& Vojnović, M.. (2018). Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans. in Acta Chimica Slovenica Slovensko Kemijsko Drustvo, Ljubljana., 65(3), 483-491. https://doi.org/10.17344/acsi.2017.3532
Kovacević S, Karadzić M, Podunavac-Kuzmanović S, Jevrić L, Ivanović E, Vojnović M. Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans. in Acta Chimica Slovenica. 2018;65(3):483-491. doi:10.17344/acsi.2017.3532 .
Kovacević, Strahinja, Karadzić, Milica, Podunavac-Kuzmanović, Sanja, Jevrić, Lidija, Ivanović, Evica, Vojnović, Matilda, "Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans" in Acta Chimica Slovenica, 65, no. 3 (2018):483-491, https://doi.org/10.17344/acsi.2017.3532 . .