Neural network modelling of antifungal activity of a series of oxazole derivatives based on in silico pharmacokinetic parameters

Kovačević, Zoran; Podunavac-Kuzmanovic, O Sanja; Jevric, R Lidija; Kalajdzija, D Natasa

doi:10.2298/apt1344249k

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…The antioxidant activity data were first normalized using min‐max normalization method:

y_{norm} = (1 - {normalΔ}^{U} - {normalΔ}^{L}) \cdot (\frac{y - y_{min}}{y_{max} - y_{min}}) + {normalΔ}^{L}

where

y_{norm}

y_{max},

and

y_{min}

are normalized, maximum, and minimum value of dependent variable y and

Δ^{U}

and

Δ^{L}

are the values of the margins that limit extrapolation ability for network. Without normalization, training process would be very slow (Kovačević, Podunavac‐Kumanović, Jevrić, & Kalajdžija, ).…”

Section: Methodsmentioning

confidence: 99%

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Artificial neural network modeling of the antioxidant activity of lettuce submitted to different postharvest conditions

Banjac

Kоvаčеvić

Jevrić

et al. 2019

J Food Process Preserv

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Using novel ebb and flow hydroponic system lettuce (Lactuca sativa L.) samples were grown and then stored under different postharvest conditions (greenhouse with a cube, refrigerator with a cube, and refrigerator without a cube). The effect of the postharvest conditions on bioactive compounds profile and antioxidant activity was studied. All samples were subjected to the bioactive compounds composition analysis as well as to the antioxidant activity determination in order to reveal how the postharvest conditions affect them. The experimental data were used for the antioxidant activity modeling by artificial neural network (ANN) approach. The antioxidant activity was determined by DPPH assay and expressed as IC50. According to the standard statistical parameters, comparison of the experimentally observed and predicted data, and residuals analysis, generated ANNs could be used for the prediction of the antioxidant activity. Practical applications Lettuce (Lactuca sativa L.) samples were grown using novel Ebb and Flow hydroponic system. Different postharvest conditions were used for samples storage. Generated models for the prediction of antioxidant activity were obtained using artificial neural network (ANN) modeling.

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“…The antioxidant activity data were first normalized using min‐max normalization method:

y_{norm} = (1 - {normalΔ}^{U} - {normalΔ}^{L}) \cdot (\frac{y - y_{min}}{y_{max} - y_{min}}) + {normalΔ}^{L}

where

y_{norm}

y_{max},

and

y_{min}

are normalized, maximum, and minimum value of dependent variable y and

Δ^{U}

and

Δ^{L}

Section: Methodsmentioning

confidence: 99%

“…where y norm , y max , and y min are normalized, maximum, and minimum value of dependent variable y and Δ U and Δ L are the values of the margins that limit extrapolation ability for network. Without normalization, training process would be very slow (Kovačević, Podunavac-Kumanović, Jevrić, & Kalajdžija, 2013).…”

Section: Chemometric Analysismentioning

confidence: 99%

Artificial neural network modeling of the antioxidant activity of lettuce submitted to different postharvest conditions

Banjac

Kоvаčеvić

Jevrić

et al. 2019

J Food Process Preserv

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“…In comparison with the results of QSAR analysis of oxazolo [4,5-b]pyridines and benzoxazoles previously published in literature, 13,14 the results described in the present paper are based on non-linear prediction of their antifungal activity based on topological and electrostatic descriptors, while in the previous studies 13 the linear modeling (PCR and PLS) of the antifungal activity have been carried out on the basis of some physicochemical and lipophilicity descriptors, as well as non-linear prediction (ANN) of antifungal activity based on some ADME descriptors. 14 The presented results emphasized the influence of electrostatic and topological molecular features on the antifungal activity based on the established non-linear models. These models can be considered slightly statistically better than the models presented in literature.…”

Section: Discussionmentioning

confidence: 80%

“…These models can be considered slightly statistically better than the models presented in literature. 13,14…”

Section: Discussionmentioning

confidence: 99%

“…The existing QSARs have been defined on the basis of MLR, PCR and PLS regression methods applying lipophilicity and some physicochemical descriptors. [11][12][13][14] The ANN method with absorption, distribution, metabolism and excretion (ADME) descriptors was applied for the same purpose as well. 14 However, this study explores the importance of topological and electrostatic characteristics of a series of benzoxazoles and oxazolo [4,5-b]pyridines in prediction of their antifungal activity toward Candida albicans.…”

Section: Introductionmentioning

confidence: 99%

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

Kоvаčеvić

Karadžić

Podunavac‐Kuzmanović

et al. 2018

ACSi

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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.

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Non-linear assessment of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives – Chemometric guidelines for further syntheses

Kоvаčеvić¹,

Podunavac‐Kuzmanović²,

Jevrić³

et al. 2014

European Journal of Pharmaceutical Sciences

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Neural network modelling of antifungal activity of a series of oxazole derivatives based on in silico pharmacokinetic parameters

Cited by 5 publications

References 15 publications

Artificial neural network modeling of the antioxidant activity of lettuce submitted to different postharvest conditions

Artificial neural network modeling of the antioxidant activity of lettuce submitted to different postharvest conditions

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

Non-linear assessment of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives – Chemometric guidelines for further syntheses

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