Journal of Physical and Theoretical Chemistry
Volume:18 Issue: 1, Spring 2021

QSAR investigations of some platinum (IV) derivatives were conducted using multiple linear regression (MLR) and artificial neural network (ANN) as modelling tools, along with simulated annealing (SA) and genetic algorithm (GA) optimization algorithms. In addition, CORAL software was used to correlate the biological activity to the structural parameters of the drugs. The obtained results from different approaches were compared and GA-ANN combination showed the best performance according to its correlation coefficient (R2) and mean sum square errors (RMSE). From the GA-ANN method, it was revealed that MTAS8e, ESpm05d, BElv3, MWC09, ESpm14u, BEHe2, RDF125e, and S3K are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double bond, present of Platinum, number of chlorine connected to Pt, branching in molecular skeleton and presence of N and O atoms are the most important molecular features affecting the biological activity of the drug. It was concluded that simultaneous utilization of QSAR and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities.

Megestrol drug is a synthetic steroid progesterone, and it is used as an anti-plasma agent to treat advanced breast cancer or endometriosis. Although the liquid chromatographic method for measuring megestrol has advantages such as excellent accuracy and reproducibility, it has limitations such as long-time measure, high equipment cost, and maintenance and use. In this study, for determination of megestrol drug in solution using kinetic spectrophotometric method, we prepared a solution of Albizia Lebbeck Leaves-capped AgNPs utilizing sodium borohydride as a stabilizer sensor. The calibration curve was linear in the range of (0.1 to 10.0 µg L−1). The standard deviation of (3.0%), and detection limit of the method (0.2 µg L−1 in time 7 min, 385 nm) were obtained for Sensor level response Albizia Lebbeck Leaves-capped AgNPs with (95%) confidence evaluated. The observed outcomes confirmed the suitability recovery and a very low detection limit for measuring the megestrol drug. The method introduced to measure megestrol drug in real samples such as urine and blood was used and can be used for hospital samples. The chemical Albizia Lebbeck Leaves-capped AgNPs sensor made it possible as an excellent sensor with reproducibility.

Essential Oils are highly concentrated substances the subtle, aromatic and volatile liquids. The use of essential oils is largely widespread in foods, deodorants, pharmaceuticals, drinks, cosmetics, medicine and embalming antiseptics especially with aromatherapy becoming increasingly popular. The lipophilicity of an organic compound can be described by a partition coefficient, logP, which plays a significant role in drug discovery and compound design. A data set of 40 compounds in the essential oil of kesum was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GA-ANN) were employed to design the Quantitative Structure-Property Relationship (QSPR) models. The predictive powers of the QSPR model was discussed using Coefficient of determination (R2), Absolute Average Deviation (AAD) and the Mean Squared Error (MSE). The R2 and MSE values of the MLR model were calculated to be 0.734 and 0.194 respectively. The R2 and MSE values for the training set of the ANN model were calculated to be 0.9905 and 2×10-4 respectively. Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method

Zn(OH)2 nanoparticles (Zn(OH)2-NPs) were sonochemically synthesized. Very low amount of Zn(OH)2-NPs was loaded on activated carbon with weight ratio of 1:10 followed by the characterization using FT-IR, XRD and SEM. Zn(OH)2 nanoparticle-loaded activated carbon (Zn(OH)2-NP-AC) as safe, green and cost-effective adsorbents were used for the removal of methyl paraben (MP). Also, the impacts of variables including initial MP dye concentration (X1), pH (X2), adsorbent dosage (X3), sonication time (X4) came under scrutiny using central composite design (CCD) under response surface methodology (RSM). The experiments have been designed utilizing response surface methodology. In this current article the values of 12 mgL-1, 0.03 g, 7.0, 4.0 min were considered as the ideal values for MP dye concentration, adsorbent mass, pH value and contact time respectively. The rapid adsorption process at neutral pH using very small amount of the adsorbent makes it promising for the wastewater treatment applications. More than 99.5% of methyl paraben was removed with maximum adsorption capacities 100 mgg−1 for MP. The kinetics and isotherm studies showed that the second-order and Langmuir models apply for the kinetics and isotherm of the adsorption of MP dye on the adsorbent used here. The adsorbent was shown to be well regenerable for several times. The short-time adsorption process, high adsorption capacity and the well regenerability of the safe, green and cost-effective Zn(OH)2-NP-AC make it advantageous and promising for the aqueous solutions.

Nowadays, heavy metal pollutants are among the challenges facing humans and the environment. Bentonite/Fe3O4 nanoparticles and sodium alginate have high efficiency for adsorption of heavy metal ions. For the purpose, four groups of bentonite magnetic nanoparticles were synthesized at different weight ratios and the prepared magnetic nanoparticles were immobilized on alginate to form calcium alginate beads for elimination of cadmium ions in the synthetic pollutant solution in different condition of adsorbent dosage, pH, stirring rate, pollutant concentration and time of adsorption. The results show that the optimum condition for removal of Cd2+ was using 0.15 g of the prepared magnetic alginate/bentonite beads containing 0.4 g magnetic nano bentonite, duration of 8 h for adsorption time and stirring rate of 200 rpm. Furthermore, adsorption efficiency of the beads for adsorption of 100 ppm of Cd2+ solution was achieved about 98% and the adsorption of Cd2+ by using alginate magnetic beads following Freundlich isotherm model.

A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculated using DRAGON software and the Genetic Algorithm (GA) and backward selection procedure were used to reduce and select the suitable descriptors. Multiple Linear Regression (MLR) analysis was carried out to derive QSPR models, which were further evaluated for statistical significance such as squared correlation coefficient (R2) root mean square error (RMSE), adjusted correlation coefficient (R2adj) and fisher index of quality (F).The multicollinearity of the descriptors selected in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC) and the Durbin–Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The best QSPR models for prediction the Cv(J/molK) and S(J/molK), having squared correlation coefficient R2 =0.907 and 0.901, root mean squared error RMSE=2.019 and RMSE= 2.505, and cross-validated squared correlation coefficient R2 cv = 0.902 and 0.889, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of new 2-Phenylindole derivatives.