Functionalization of nitrogen-doped graphene quantum dot: A sustainable carbon-based catalyst for the production of cyclic carbonate from epoxide and CO2.

A series of organic compounds were successfully immobilized on an N-doped graphene quantum dot (N-GQD) to prepare a multifunctional organocatalyst for coupling reaction between CO2 and propylene oxide (PO). The simultaneous presence of halide ions in conjunction with acidic- and basic-functional groups on the surface of the nanoparticles makes them highly active for the production of propylene carbonate (PC). The effects of variables such as catalyst loading, reaction temperature, and structure of substituents are discussed. The proposed catalysts were characterized by different techniques, including Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy/energy dispersive X-ray microanalysis (FESEM/EDX), thermogravimetric analysis (TGA), elemental analysis, atomic force microscopy (AFM), and ultraviolet-visible (UV-Vis) spectroscopy. Under optimal reaction conditions, 3-bromopropionic acid (BPA) immobilized on N-GQD showed a remarkable activity, affording the highest yield of 98% at 140°C and 106 Pa without any co-catalyst or solvent. These new metal-free catalysts have the advantage of easy separation and reuse several times. Based on the experimental data, a plausible reaction mechanism is suggested, where the hydrogen bonding donors and halogen ion can activate the epoxide, and amine functional groups play a vital role in CO2 adsorption.

A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques.

Quantitative structure-activity relationships (QSAR) and molecular docking studies have been performed on a series of 35 α-glucosidase inhibitory derivatives. The QSAR models have been developed by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods to correlate the conformational descriptors to the inhibitory activity. The obtained models with 5 descriptors were validated and illustrated to be statistically significant. They had desirable prediction based on squared correlation coefficient (R2), cross-validated correlation coefficient (Q2), root-mean-squares error (RMSE) and Fisher (F) parameters (R2 = 0.951, Q2 = 0.931, RMSE = 0.121, and F = 114.629 for GA-MLR model, and R2 = 0.989, Q2 = 0.987, RMSE = 0.056 and F = 543.754 for LS-SVM model). The crucial descriptor named DELS was explored to have the highest correlation with the inhibitory activity and thus has been chosen to build a simple model. The QSAR model developed with this mono-descriptor showed appropriate results of the predicted model using LS-SVM method (R2 = 0.888, Q2 = 0.872, RMSE = 0.185 and F = 221.459). Also, molecular docking which focuses on the interaction between ligands and α-glucosidase in the protein active site considered different binding positions to find the best binding mode. It helped the QSAR study to propose more comprehensive details of the compounds structures and was used to design more active compounds. The most active designed compound had a high inhibitory activity of 9.22 that can be proposed for the treatment of diabetes type 2.

Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies.

Poly ADP-ribose polymerase-1 (PARP-1) inhibitors have been recognized as new agents for the treatment of patients with breast cancer type 1 (BRCA1) disorders. The quantitative structure-activity relationships (QSAR) technique was used in order to achieve the required medicines for anticancer activity easier and faster. In this study, the QSAR method was developed to predict the half-maximal inhibitory concentration (IC50) of 51 1H-benzo[d]immidazole-4-carboxamide derivatives by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods. Results in the best QSAR model represented the coefficient of leave-one-out cross-validation (Q cv 2 ) = 0.971, correlation coefficient (R2) = 0.977, Fisher parameter (F) = 259.016 and root mean square error (RMSE) = 0.095, respectively, which indicated that the LS-SVM model had a good potential to predict the pIC50 (9 - log(IC50 nM)) values compared with other modeling methods. Also, molecular docking evaluated interactions between ligands and enzyme and their free energy of binding were calculated and used as descriptors. Molecular docking and the QSAR study completed each other. The results represented that the final model can be useful to design some new inhibitors. So, the knowledge of the QSAR modeling and molecular docking was used in pIC50 prediction and 51 new compounds were developed as PARP-1 inhibitors that 9 compounds had the best-proposed values for pIC50. The maximum enhancement of the inhibitory activity of compounds was 33.394%.

Modification of multi-walled carbon nanotubes by 1,3-diaminopropane to increase CO2 adsorption capacity.

Greenhouse gas emissions have increased dramatically over the past years and had a significant impact on global warming. This study investigates the modification of multi-walled carbon nanotubes (MWCNTs) with diamine precursor to improve the carbon dioxide adsorption capacity. To achieve this goal, pristine multi-walled CNTs were functionalized in a two-step process. In the first step, multi-walled carbon nanotubes were functionalized with a mixture of diluted sulfuric and nitric acid (5 M HNO3/5 M H2SO4 with a volume ratio of 1:3) to sequestrate catalytic metal particles and oxidation of MWCNTs. In the second step, oxidized carbon nanotubes were functionalized with 1,3-diaminopropane (DAP) solution to improve the performance of multi-walled CNT in the carbon dioxide adsorption process. Specifications and characteristics of raw and modified carbon nanotubes were determined using FTIR, SEM, TGA, XRD, and N2 adsorption-desorption isotherms at 77 K. The CO2 adsorption capacity was measured at 303-323 K and pressures up to 17.3 bar using volumetric method. At 303 K and pressure of 17.3 bar, 92.71 mg g-1 of CO2 was adsorbed on MWCNT/DAP, while the CO2 uptake of raw MWCNT in similar conditions was just 48.49 mg g-1. The results revealed that amine groups attached to the carbonaceous surfaces during the functionalization process cause the formation of carbon dioxide-adsorption sites on multi-walled CNTs which increased the adsorption capacity of MWCNTs. Experimental data was modeled with Langmuir and Freundlich adsorption isotherms and concluded that the Freundlich model has more fitness with the experimental data.

Development of 3-hydroxybutyrate dehydrogenase enzyme biosensor based on carbon nanotube-modified screen-printed electrode.

Precise detection of 3-hydroxybutyrate (HB) in biological samples is of great importance for management of diabetic patients. In this study, an HB biosensor based on single-walled carbon nanotubes (SWCNTs)-modified screen-printed electrode (SPE) was developed to determine the concentration of HB in serum. The specific detecting enzyme, HB dehydrogenase, was physically immobilised on SWCNTs deposited on the surface of SPEs. The electrochemical measurement of HB that involved cyclic voltammetry was based on the sAgnal produced by j3-nicotinamide adenine dinucleotide (NADH), one of the products of the enzymatic reaction. The application of SWCNT reduced the oxidation potential of NADH to about -0.05 V. Electrochemical measurements showed that the response of this biosensor had relevant good linearity in the range of 0.1-2 mM with a low detection limit of 0.009 mM. Investigation of biosensor response in the presence of interfering molecules verified its specificity. Furthermore, the study of long-term stability demonstrated the acceptable efficiency of this biosensor for about 100 days.

Highlighting and trying to overcome a serious drawback with QSPR studies; data collection in different experimental conditions (mixed-QSPR).

The experimental conditions in quantitative structure-property relationship (QSPR) studies need to be the same for each dataset in case one wishes to relate the property, only to the structure. This major drawback limits QSPR studies due to two reasons: (1) Gathering of physicochemical data obtained under the same experimental condition is difficult. (2) The obtained model is just useful to predict the physicochemical properties under the specific experimental condition. In this article, we report an attempt to highlight the shortcoming of QSPR studies for a property that was measured under different experimental conditions. In addition, we reveal inadequacies that correlating the fluorescence properties and the descriptor of the solvent has. These defects are eventually removed by taking into account the solvent-solute interactions in descriptor calculations. Quantum chemical calculations (HF/6-31G*) were carried out to optimize geometry and calculate the structural descriptors. The genetic algorithm combined with multiple linear regression method was utilized to construct the linear QSPR models. Because of the better nonlinear relationship between the quantum yield of fluorescence and structural descriptors in comparison with those of a linear relationship, support vector machine was used to construct the nonlinear QSPR model. Result analyses demonstrated that the proposed models meet our goal.

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture.

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories.

Interaction study of pioglitazone with albumin by fluorescence spectroscopy and molecular docking.

Pioglitazone is a medicine of thiazolidinedione (TZD) class with hypoglycemic (antihyperglycemic, antidiabetic) action. Pioglitazone binding to human serum albumin (HSA) was investigated at different temperatures (290, 300 and 310 K) by fluorescence spectroscopic method. Molecular docking study was also carried out besides the experiments. Experimental results revealed that pioglitazone have an ability to quench the intrinsic fluorescence of HSA tryptophan through a static quenching procedure. The binding constant was determined using Stern-Volmer modified equation and energy transfer mechanisms of quenching were discussed. Thermodynamic parameters were also calculated according to enthalpy changes dependence on different temperatures. According to the theoretical and experimental results, hydrogen bonding was found to play a major role in the interaction of pioglitazone with HSA.

QSAR study of C allosteric binding site of HCV NS5B polymerase inhibitors by support vector machine.

Multiple linear regressions (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship (QSAR) models of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as chemical, topological, geometrical, and quantum descriptors. Principal component analysis (PCA) was used to select the training set. A variable selection method utilizing a genetic algorithm (GA) was employed to select from the large pool of calculated descriptors, an optimal subset of descriptors which have significant contribution to the overall inhibitory activity. The models were validated using Leave-One-Out (LOO) and Leave-Group-Out (LGO) crossvalidation, and Y-randomization test. Results demonstrated the SVM model offers powerful prediction capabilities.

Computational studies on effects of MDMA as an anticancer drug on DNA.

This research is designed to further understand the effects of the novel drug MDMA on biologic receptor of DNA. The ultimate goal is to design drugs that have higher affinity with DNA. Understanding the physicochemical properties of the drug as well as the mechanism by which it interacts with DNA should ultimately enable the rational design of novel anticancer or antiviral drugs. Molecular modeling on the complex formed between MDMA and DNA presented this complex to be fully capable of participating in the formation of a stable intercalation site. Furthermore, the molecular geometries of MDMA and DNA bases (Adenine, Guanine, Cytosine, and Thymine) were optimized with the aid of the B3LYP/6-31G* method. The properties of the isolated intercalator and its stacking interactions with adenine···thymine (AT) and guanine···cytosine (GC) nucleic acid base pairs were studied with the DFTB method. DFTB method is an approximate version of the DFT method that was extended to cover the London dispersion energy. The B3LYP/6-31G* stabilization energies of the intercalator···base pair complexes were found to be -9.40 and -12.57 kcal/mol for AT···MDMA and GC···MDMA, respectively. Results from comparison of the DFTB method and HF method conclude close results and support each other.

Quantitative structure-activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method.

A linear quantitative structure-activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently discovered amides and imidazo[1,2- α] pyridines. Among the different constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors that were considered as inputs to the model, seven variables were selected using the genetic algorithm subset selection method (GA). The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds.

Simultaneous spectrophotometric determination of 2-thiouracil and 2-mercaptobenzimidazole in animal tissue using multivariate calibration methods: concerns and rapid methods for detection.

Two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), were applied to the spectrophotometric simultaneous determination of 2-mercaptobenzimidazole (MB) and 2-thiouracil (TU). A genetic algorithm (GA) using partial least squares was successfully utilized as a variable selection method. The concentration model was based on the absorption spectra in the range of 200 to 350 nm for 25 different mixtures of MB and TU. The calibration curve was linear across the concentration range of 1 to 10 microg mL(-1) and 1.5 to 15 microg mL(-1) for MB and TU, respectively. The values of the root mean squares error of prediction (RMSEP) were 0.3984, 0.1066, and 0.0713 for MB and 0.2010, 0.1667, and 0.1115 for TU, which were obtained using PCR, PLS, and GA-PLS, respectively. Finally, the practical applicability of the GA-PLS method was effectively evaluated by the concurrent detection of both analytes in animal tissues. It should also be mentioned that the proposed method is a simple and rapid way that requires no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories.

QSAR study on melanocortin-4 receptors by support vector machine.

Quantitative structure activity relationship (QSAR) of the melanocortin-4 receptor (MC4R) binding affinities (K(i)) of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides of piperazinecyclohexanes was studied. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors that resulted in the best-fit models. The multiple linear regression (MLR), and the support vector machine (SVM) were utilized to construct the linear and nonlinear QSAR models. The models were validated using Leave-One-Out (LOO) and Leave-Group-Out (LGO) cross-validation, external test set, and chance correlation. The SVM model generalizes better than the MLR model. The SVM model, with high statistical significance (R(2)(train)=0.908, Q(2)(LOO)=0.781, Q(2)(LGO)=0.872), could be used to predict melanocortin-4 receptor binding affinities of piperazinecyclohexanes.

Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity.

The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure-activity relationship (QSAR) for BK-channel activators. The data set was divided into 57 molecules of training and 14 molecules of test sets. A large number of descriptors were calculated and genetic algorithm (GA) was used to select variables that resulted in the best-fitted for models. A comparison between the obtained results using SVM with those of multi-parameter linear regression (MLR) revealed that SVM model was much better than MLR model. The improvements are due to the fact that the activity of the compounds demonstrates non-linear correlations with the selected descriptors. Also distances between Oxygen and Chlorine atoms, the mass, the van der Waals volume, the electronegativity, and the polarizability of the molecules are the main independent factors contributing to the BK-channels activity of the studied compounds.

Quantum chemical calculations to reveal the relationship between the chemical structure and the fluorescence characteristics of phenylquinolinylethynes and phenylisoquinolinylethynes derivatives, and to predict their relative fluorescence intensity.

In this paper the relationship between the chemical structure and fluorescence characteristics of 30 phenylquinolinylethyne (PhQE), and phenylisoquinolinylethyne (PhIE) derivatives compounds employing ab initio calculations have been elucidated. Quantum chemical calculations (6-31G) were carried out to obtain: the optimized geometry, energy levels, charges and dipole moments of these compounds, in the singlet (steady and excited states) and triplet states. The relationship between quantum chemical descriptors, and wavelength of maximum excitation and emission indicated that these two parameters have the most correlation with quantum chemical hardness (eta). Also, stokes shift has the most correlation with the square of difference between the maximum of positive charges in the singlet steady and singlet excited states. The quantitative structure-property relationship (QSPR) of PhQE and PhIE was studied for relative fluorescence intensity (RFI). The genetic algorithm (GA) was applied to select the variables that resulted in the best-fit models. After the variable selection, multiple linear regression (MLR) and support vector machine (SVM) were both utilized to construct linear and non-linear QSPR models, respectively. The SVM model demonstrated a better performance than that of the MLR model. The route mean square error (RMSE) in the training and the test sets for the SVM model was 0.195 and 0.324, and the correlation coefficients were 0.965 and 0.960, respectively, thus revealing the reliability of this model. The resulting data indicated that SVM could be used as a powerful modeling tool for QSPR studies. According to the best of our knowledge, this is the first research on QSPR studies to predict RFI for a series of PhQE and PhIE derivative compounds using SVM.

Determination of the formation constant for the inclusion complex between Lanthanide ions and Dansyl chloride derivative by fluorescence spectroscopy: theoretical and experimental investigation.

In this paper, a sensitive, easy, efficient, and suitable method for the calculation of K(f) values of complexation between one derivative of Dansyl chloride [5-(dimethylamino) naphthalene-1-sulfonyl 4-phenylsemicarbazide] (DMNP) and Lanthanide(III) (Ln) ions is proposed, using both spectrofluorometric and spectrophotometric methods. Determination of K(f) showed that DMNP was mostly selective towards the erbium (III) ion. The validity of the method was also confirmed calculating the Stern-Volmer fluorescence quenching constants (K(sv)) that resulted in the same consequence, obtained by calculating the K(f) of complexation values. In addition, the UV-vis spectroscopy was applied for the determination of K(f) only for the Ln ions that had interactions with DMNP. Finally, the DFT studies were done on Er(3+) and the DMNP complex for distinguishing the active sites and estimating the pair wise interaction energy. It can be concluded that this derivative of Dansyl chloride with inherent high fluorescence intensity is a suitable reagent for the selective determination of the Er(3+) ion which can be used in constructing selective Er(3+) sensors.

Quantitative structure-activity relationship study on the anti-HIV-1 activity of novel 6-naphthylthio HEPT analogs.

The quantitative structure-activity relationship of the novel 6-naphthylthio 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio) thymine derivatives for prediction of anti-human immunodeficiency virus type 1 activity was studied. The suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections of the stepwise multiple linear regression and the genetic algorithm were selected. A comparison between the attained results indicated the superiority of the genetic algorithm over the stepwise multiple regression method in the feature-selection. The predictive quality of the quantitative structure-activity relationship models was tested for an external set of eight compounds, randomly chosen out of 39 compounds. The genetic algorithm-multiple linear regression model with six selected descriptors was obtained. This model, demonstrating high statistical qualities (R(2)(train) = 0.925, Q(2) = 0.872, SE (%) = 1.23, F = 49.338, R(2)(pred) = 0.944), could predict the anti-human immunodeficiency virus type 1 activity of the molecules with a prediction error percentage lower than 10%. The results suggest that electronegativity, the masses, and the atomic van der Waals volumes are the main independent factors contributing to the anti-human immunodeficiency virus type 1 activity of the studied compounds.

Support vector machine-based quantitative structure-activity relationship study of cholesteryl ester transfer protein inhibitors.

To explore inhibition of cholesteryl ester transfer protein, a support vector machine in quantitative structure-activity relationship was developed for modeling cytotoxicity data for a series of cholesteryl ester transfer protein inhibitors. A large number of descriptors were calculated and genetic algorithm was used to select variables that resulted in the best-fitted models. The data set was randomly divided into 68 molecules of training and 17 molecules of test set. The selected molecular descriptors were used as inputs for support vector machine. The obtained results using support vector machine were compared with those of multiple linear regression which revealed superiority of the support vector machine model over the multiple linear regression. The root mean square errors of the training set and the test set for support vector machine model were calculated to be 3.707, 5.273 and the correlation coefficients (r(2)) were obtained to be 0.947, 0.899, respectively. The obtained statistical parameter of leave-one-out cross-validation test correlation coefficients (q(2)) on support vector machine model was 0.852, which indicates the reliability of the proposed model.

Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine.

The quantitative structure-retention relationship (QSRR) of the essential oil components against the gas chromatography retention index (RI) was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions [e.g. the multiple linear regression (MLR), the partial least squares (PLS)] as well as the nonlinear regressions [e.g. the polynomial PLS (poly-PLS), the support vector machine (SVM)] were utilized to construct the linear and nonlinear QSRR models. The obtained results using SVM were compared with those of MLR, PLS and poly-PLS, exhibiting that the SVM model demonstrated a better performance than that of the other models. The relative standard error SE (%) of the training set and the test set for the SVM model was 1.96 and 4.25, and the square correlation coefficients were 0.987 and 0.962 respectively, while the square correlation coefficient of the cross validation (Q(2)) on the SVM model was 0.963, revealing the reliability of this model. The resulting data indicated that SVM could be used as a powerful modeling tool for the QSRR studies. This is the first research on the QSRR of the essential oil compounds against the retention index using the SVM.

A computational approach to studying monomer selectivity towards the template in an imprinted polymer.

A computational approach was proposed to study monomer-template interactions in a molecularly imprinted polymer (MIP) in order to gain insight at the molecular level into imprinting polymer selectivity, regarding complex formation between template and monomer at the pre-polymerisation step. This is the most important step in MIP preparation. In the present work, chlorphenamine (CPA), diphenhydramine (DHA) and methacrylic acid (MAA), were chosen as the template, non-template, and monomer, respectively. The attained complexes were optimised, and changes in the interaction energies, atomic charges, IR spectroscopy results, dipole moment, and polarisability were studied. The effects of solvent on template-monomer interactions were also investigated. According to a survey of the literature, this is the first work in which dipole moment and polarisability were used to predict the types of interactions existing in pre-polymerisation complexes. In addition, the density functional tight-binding (DFTB) method, an approximate version of the density functional theory (DFT) method that was extended to cover the London dispersion energy, was used to calculate the interaction energy.