Study of the correlation between chemical structure, physicochemical and retention parameters of newly synthesized s-triazine derivatives by reversed-phase chromatography

Primenom tečne hromatografije na obrnutim fazama ispitano je retenciono ponašanje 14 novosintetisanih derivata s-triazina na tankom sloju RP C-18 i silika gelu impregniranim parafinskim uljem. Kao pokretne faze korišćene su dvokomponentne smeše vode i organskih rastvarača. Izračunate su RM0 vrednosti i ispitana je korelacija sa različitim deskriptorima i prediktorima biološke aktivnosti.Retentional behaviour of 14newly syntetised derivates of s-triazine on RP C-18 and paraffin oil-impregnated silica gel suport had been investigated using thin-layer  chromatography. As a mobilephases were used two-component water based mobile  phases. Calculated RM0 values were corelated to various molecular descriptors  as well as biological activity indicators

Study of the correlation between chemical structure, physicochemical and retention parameters of newly synthesized s-triazine derivatives by reversed-phase chromatography

Primenom tečne hromatografije na obrnutim fazama ispitano je retenciono ponašanje 14 novosintetisanih derivata s-triazina na tankom sloju RP C-18 i silika gelu impregniranim parafinskim uljem. Kao pokretne faze korišćene su dvokomponentne smeše vode i organskih rastvarača. Izračunate su RM0 vrednosti i ispitana je korelacija sa različitim deskriptorima i prediktorima biološke aktivnosti.Retentional behaviour of 14newly syntetised derivates of s-triazine on RP C-18 and paraffin oil-impregnated silica gel suport had been investigated using thin-layer  chromatography. As a mobilephases were used two-component water based mobile  phases. Calculated RM0 values were corelated to various molecular descriptors  as well as biological activity indicators

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

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

Reversed-phase thin-layer chromatography behavior of aldopentose derivatives

Quantitative structure-retention relationships (QSRR) have been used to study the chromatographic behavior of some aldopentose. The behavior of aldopentose derivatives was investigated by means of the reversed-phase thin-layer chromatography (RP TLC) on the silica gel impregnated with paraffin oil stationary phases. Binary mixtures of methanol-water, acetone-water and dioxane-water were used as mobile phases. Retention factors, RM0, corresponding to zero percent organic modifier in the aqueous mobile phase was determined. Lipophilicity C0 was calculated as the ratio of the intercept and slope values. There was satisfactory correlation between them and log P values calculated using different theoretical procedures. Some of these correlations offer very good predicting models, which are important for a better understanding of the relationships between chemical structure and retention. The study showed that the hydrophobic parameters RM0 and C0 can be used as a measures of lipophilicity of investigated compounds

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