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

In the present paper, the antifungal activity of a series of benzoxazole and oxazolo[ 4,5-b]pyridine derivatives was evaluated against Candida albicans by using quantitative structure-activity relationships chemometric methodology with artificial neural network (ANN) regression approach. In vitro antifungal activity of the tested compounds was presented by minimum inhibitory concentration expressed as log(1/cMIC). In silico pharmacokinetic parameters related to absorption, distribution, metabolism and excretion (ADME) were calculated for all studied compounds by using PreADMET software. A feedforward back-propagation ANN with gradient descent learning algorithm was applied for modelling of the relationship between ADME descriptors (blood-brain barrier penetration, plasma protein binding, Madin-Darby cell permeability and Caco-2 cell permeability) and experimental log(1/cMIC) values. A 4-6-1 ANN was developed with the optimum momentum and learning rates of 0.3 and 0.05, respectively. An excellent correlation between experimental antifungal activity and values predicted by the ANN was obtained with a correlation coefficient of 0.9536. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

Correlation and principal component analysis in ceramic tiles characterization

The present study deals with the analysis of the characteristics of ceramic wall and floor tiles on the basis of their quality parameters: breaking force, flexural strenght, absorption and shrinking. Principal component analysis was applied in order to detect potential similarities and dissimilarities among the analyzed tile samples, as well as the firing regimes. Correlation analysis was applied in order to find correlations among the studied quality parameters of the tiles. The obtained results indicate particular differences between the samples on the basis of the firing regimes. However, the correlation analysis points out that there is no statistically significant correlation among the quality parameters of the studied samples of the wall and floor ceramic tiles.[Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. III 45008

A chemometric approach for prediction of antifungal activity of some benzoxazole derivatives against Candida albicans

The purpose of the article is to promote and facilitate prediction of antifungal activity of the investigated series of benzoxazoles against Candida albicans. The clinical importance of this investigation is to simplify design of new antifungal agents against the fungi which can cause serious illnesses in humans. Quantitative structure activity relationship analysis was applied on nineteen benzoxazole derivatives. A multiple linear regression (MLR) procedure was used to model the relationships between the molecular descriptors and the antifungal activity of benzoxazole derivatives. Two mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds against Candida albicans. The quality of the models was validated by the leave-one-out technique, as well as by the calculation of statistical parameters for the established model. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

Molecular docking analysis of newly synthesized 2- morpholinoquinoline derivatives with antifungal potential toward Aspergillus fumigatus

The present paper is concerned with the molecular docking analysis of newly synthesized 2-morpholinoquinoline derivatives with antifungal potential toward Aspergillus fumigatus. The purpose of the molecular docking analysis was to determine potential interactions between the analyzed compounds and the active site of the enzyme glucosamine-6-phosphate synthase, as well as to reveal which molecular features (the presence of different substituents or isomers) could be responsible for significant antifungal activity of the tested compounds. The compounds with the highest antifungal potential toward pathogenic and saprotrophic fungus Aspergillus fumigatus were docked, and the results were compared with the docking of griseofulvin, which is an antifungal drug used in the treatment of various types of dermatophytoses. Newly discovered antifungal agents are very important in medicine, as well as in agriculture. The prevention of the presence of mycotoxins in food and feed products is one of the urgent tasks. Therefore, every effort which leads to discovery of their mechanism of action and determination of side effects is precious. The present study can be considered a contribution to the analysis and selection of newly discovered antifungals from the 2-morpholinoquinoline family, and one step forward to their practical use in medicine and agriculture. The obtained results reveal the possible reason why the newly synthesized 2-morpholinoquinoline expresses even higher antifungal activity toward Aspergillus fumigatus than griseofulvin, a commercially available antifungal therapeutic

Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents

Quantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the principal component analysis (PCA). The partial least square regression (PLSR) was used to distinguish the most important molecular descriptors for non-linear modeling. Artificial neural networks (ANNs) were applied for the antifungal activity prediction. The best QSAR models were validated by statistical parameters and graphical methods. High agreement between the observed and predicted antifungal activity values indicated the good quality of the derived QSAR models. The obtained QSAR-ANN models can be used to predict the antifungal activity of dihydroindeno and indeno thiadiazines and of structurally similar compounds. The modeling of the antifungal activity can contribute to the synthesis of new antifungal agents with better ability to protect food and feed from the mycotoxins

Linear and Nonlinear Structure-Retention Relationship Analysis of Different Classes of Pesticides Isolated From Groundwater

<div><p>The present article gives an insight into the linear and nonlinear relationships between the retention behavior in reversed-phase high performance liquid chromatography (RP-HPLC) of several classes of pesticides isolated from groundwater, and their <i>in silico</i> physicochemical, topological and lipophilicity molecular descriptors. The quantitative structure-retention relationship (QSRR) chemometric approach was applied for this purpose on a large set of compounds (77 pesticides). The selection of the most appropriate molecular descriptors was achieved by stepwise selection procedure coupled with partial least squares method and the variance inflation in projection parameter (SS-PLS-VIP). QSRR included the linear regression (LR), multiple linear regession (MLR), and artificial neural networks regression (ANN-R). In order to select the optimal QSRR model, statistical validation parameters were used. Additionally, a relatively new chemometric method called sum of ranking differences (SRD) was applied in order to select the optimal regression model. The obtained results showed that certain models can successfully be used for precise prediction of the retention time of the studied compounds.</p></div

Chemometric analysis of metal contents in different types of chocolates

The relationships between the contents of various metals (Cu, Ni, Pb and Al) in 38 different milk chocolate samples were studied using a chemometric approach. The chemometric expressions were generated using a training set of 25 chocolate samples and the predictive ability of the resulting models was evaluated against a test set of 13 chocolate samples. The chemometric analysis was based on the application of multiple linear regression analysis (MLR). MLR was performed in order to select the significant models for predicting the metal contents. The MLR equations that represent the content of one metal as a function of the contents of other metals were established. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. It enables the researchers to establish reliable relationships between the contents of various metals which can be used for their prediction in different types of chocolate prior to their analysis. This can reduce the trial-and-error element and experimental costs in the production.[Projekat Ministarstva nauke Republike Srbije, br. 31055, br. 172012 i br. 172014

Application of multiple linear regression analysis to predict antifungal activity of some benzimidazole derivatives using ADME parameters

In this study we were investigated the relationship between the antifungal activity of some benzimidazole derivatives and some absorption, distribution, metabolism and excretion (ADME) parameters. The antifungal activity of studied compounds against Saccharomyces cerevisiae was expressed as the minimal inhibitory concentration (MIC). A statistically significant quantitative structure-activity relationship (QSAR) model for predicting antifungal activity of the investigated benzimidazole derivatives against Saccharomyces cerevisiae was obtained by multiple linear regression (MLR) using ADME parameters. The quality of the MLR model was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed model, and the results are discussed based on the statistical data. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

Retention Data from Normal-Phase Thin-Layer Chromatography in Characterization of Some 1,6-anhydrohexose and D-aldopentose Derivatives by QSRR Method

<div><p>The relationship between retention behavior of eleven 1,6-anhydrohexose and D-aldopentose derivatives and their molecular characteristics was studied using quantitative structure–retention relationships (QSRR) approach. Retention parameters <i>R</i>_M⁰, obtained by normal-phase thin-layer chromatography, were correlated with molecular and <i>in silico</i> absorption, distribution, metabolism and excretion (ADME) descriptors. For describing the retention behavior of investigated molecules and determining the similarities between molecules, principal component analysis (PCA) was performed, followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR). For both sets of descriptors, PCA resulted in a model with the two significant principal components (PCs). HCA was conducted to confirm the grouping of the compounds already obtained by PCA. MLR equations were established for both sets of descriptors and statistical quality of the generated models was determined by standard statistical measures and cross-validation parameters. According to statistical validation, two very good models with molecular and one with <i>in silico</i> ADME descriptors were obtained. Very good predictive ability of the established mathematical models allows us to estimate retention behavior of structurally similar compounds and to understand their behavior in similar chromatographic systems.</p></div