TY  - 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 . .

TY  - JOUR
AU  - Podunavac-Kuzmanović, Sanja
AU  - Jevrić, Lidija
AU  - Svarc-Gajić, Jaroslava
AU  - Kovacević, Strahinja
AU  - Vasiljević, Ivana
AU  - Kecojević, Isidora
AU  - Ivanović, Evica
PY  - 2015
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/3795
AB  - The relationships between the contents of various metals (Cu, Ni, Pb and Al) in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate and define the strong non-linear relationship between metal contents.
PB  - Slovensko Kemijsko Drustvo, Ljubljana
T2  - Acta Chimica Slovenica
T1  - Artificial Neural Network Approach to Modelling of Metal Contents in Different Types of Chocolates
EP  - 195
IS  - 1
SP  - 190
VL  - 62
UR  - https://hdl.handle.net/21.15107/rcub_agrospace_3795
ER  - 

@article{
author = "Podunavac-Kuzmanović, Sanja and Jevrić, Lidija and Svarc-Gajić, Jaroslava and Kovacević, Strahinja and Vasiljević, Ivana and Kecojević, Isidora and Ivanović, Evica",
year = "2015",
abstract = "The relationships between the contents of various metals (Cu, Ni, Pb and Al) in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate and define the strong non-linear relationship between metal contents.",
publisher = "Slovensko Kemijsko Drustvo, Ljubljana",
journal = "Acta Chimica Slovenica",
title = "Artificial Neural Network Approach to Modelling of Metal Contents in Different Types of Chocolates",
pages = "195-190",
number = "1",
volume = "62",
url = "https://hdl.handle.net/21.15107/rcub_agrospace_3795"
}

Podunavac-Kuzmanović, S., Jevrić, L., Svarc-Gajić, J., Kovacević, S., Vasiljević, I., Kecojević, I.,& Ivanović, E.. (2015). Artificial Neural Network Approach to Modelling of Metal Contents in Different Types of Chocolates. in Acta Chimica Slovenica
Slovensko Kemijsko Drustvo, Ljubljana., 62(1), 190-195.
https://hdl.handle.net/21.15107/rcub_agrospace_3795

Podunavac-Kuzmanović S, Jevrić L, Svarc-Gajić J, Kovacević S, Vasiljević I, Kecojević I, Ivanović E. Artificial Neural Network Approach to Modelling of Metal Contents in Different Types of Chocolates. in Acta Chimica Slovenica. 2015;62(1):190-195.
https://hdl.handle.net/21.15107/rcub_agrospace_3795 .

Podunavac-Kuzmanović, Sanja, Jevrić, Lidija, Svarc-Gajić, Jaroslava, Kovacević, Strahinja, Vasiljević, Ivana, Kecojević, Isidora, Ivanović, Evica, "Artificial Neural Network Approach to Modelling of Metal Contents in Different Types of Chocolates" in Acta Chimica Slovenica, 62, no. 1 (2015):190-195,
https://hdl.handle.net/21.15107/rcub_agrospace_3795 .