3D-QSAR Modeling on 2-Pyrimidine Carbohydrazides as Utrophin Modulators for the Treatment of Duchenne Muscular Dystrophy by Combining CoMFA, CoMSIA, and Molecular Docking Studies.

The 3D-QSAR models were developed using CoMFA and CoMSIA techniques to investigate essential molecular fields, optimization strategies, and structure-activity relationships for utrophin-modulating compounds. The data set (71 molecules) was divided into two training and test sets using the hierarchical clustering approach. The training set was aligned based on the most active compound. The built and optimized models based on the PLS approach provided acceptable results. The results were q 2 = 0.528 and r 2 = 0.776 for CoMFA and q 2 = 0.600 and r 2 = 0.811 for CoMSIA models. According to the statistical results, it was found that both the CoMFA models with and without regional focusing and also the CoMSIA model have good estimation ability. Molecular docking was also performed with high-activity compounds (as ligands) and target receptors (protein), and its results, together with the results of 3D-QSAR, give new insights for the design of compounds with higher biological activity. Finally, based on the overall results, the design of new compounds with higher utrophin modulation activity was carried out.

Magnetic solid phase extraction of Sunitinib malate in urine samples assisted with mixed hemimicelle and spectrophotometric detection.

The mixed hemimicelle-based solid phase extraction method using the coated sodium dodecyl sulfate by magnetic iron oxide nanoparticles as adsorbent was developed for extraction and determination of Sunitinib malate in real samples prior to determination by UV-Visible spectrophotometry. For the characterization of synthesized nanoparticles, Fourier transform infrared spectroscopy, and scanning electron microscopy was used. The influences of different factors affecting the extraction efficiency of Sunitinib malate, including the pH, the adsorbent amount, the volume and eluent type, the amount of the surfactant, the ionic strength, extraction, and desorption time, were investigated. At the optimized conditions, a good linearity with correlation coefficients of 0.998 and 0.999 was obtained over the concentration ranges of 1-22 and 1-19 µg/mL for water and urine samples, in order. The good recoveries of 97% and 99% and also, the limits of detection equal with 0.9, and 0.8 µg/mL for water and urine samples were enhanced, respectively. These results demonstrate that mixed hemimicelle solid phase extraction is a fast, efficient, economical and selective sample preparation method for the extraction and determination of Sunitinib malate in different water and urine sample solutions.

Density function theory calculation to study the oxidation potential of electron-donating compounds; affirming the oxidation mechanism by NICS calculations.

The manuscript describes a method for understanding the correlation of structural features and first oxidation potentials [Formula: see text] of electron-donating compounds (EDCs) with tetrathiafulvalene (TTF), dithiadiazafulvalenes (DTDAF), and tetraazafulvalene (TAF) frameworks. The density functional theory (DFT) procedure at B3LYP (6-31 + g(d)) was used for geometric optimization, given the large dimensions of the molecules studied, and their high structural similarity. First of all, the correlation between the oxidation potential and the highest occupied molecular orbital (HOMO) energy level as an effective quantum chemical descriptor was examined. Then, nucleus-independent chemical shifts (NICSs) calculation was applied to affirm the oxidation mechanism and interpret the effect of replacing the sulfur atoms by nitrogen, on the oxidation process. Finally, a more comprehensive investigation of structural features that affect the oxidation potential, topological, geometrical, constitutional, as well as, electrostatic, charged partial surface area, quantum-chemical, molecular orbital, and thermodynamic descriptors was calculated. A predictive model was developed based on the genetic algorithm multivariate linear regression (GA-MLR). There was an outstanding agreement between the theoretical and the experimental values obtained for the first oxidation potentials of the test set (Q2Ext = 0.981).

Fluorescence resonance energy transfer between carbon quantum dots and silver nanoparticles: Application to mercuric ion sensing.

Fluorescence resonance energy transfer (FRET) process as a practical and competitive sensing strategy was utilized between carbon quantum dots (C-dots) and silver nanoparticles (Ag NPs) for the determination of mercuric ions. The novel synthesized C-dots with the quantum yield of 84% acted as the donor and Ag NPs operated as the acceptor in the FRET process leading to the fluorescence quenching of the C-dots. In the presence of Hg(II) ions, the FRET-quenched fluorescence emission of the C-dots-Ag NPs system was recovered owing to oxidation of Ag NPs by Hg(II) ions, so that the turn-on fluorescence intensity was directly proportional to the Hg(II) ion concentration. Accordingly, combination of the FRET system with the redox reaction was firstly utilized to construct an innovative turn-off/on fluorescent sensor for the quantification of Hg(II) ion. The calibration plot was linear in the concentration range 0.5-500.0 nmol L-1 with a determination coefficient (R2) of 0.9965. The limit of detection and limit of quantification were 0.10 and 0.35 nmol L-1, respectively, according to the IUPAC definition. The method was applied for the determination of Hg(II) ion in lake water, wastewater and tea samples, and the proper relative recoveries (98.0-104.0%) were obtained for the spiked samples. The method has high potential to diagnose trace values of mercuric ions in real samples with high sensitivity and repeatability.

QSAR Study of 17ß-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer.

The 17ß-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitors can be used to efficiently target it. In the present study, the multiple linear regression (MLR), and support vector machine (SVM) methods were used to interpret the chemical structural functionality against the inhibition activity of some 17ß-HSD3inhibitors. Chemical structural information were described through various types of molecular descriptors and genetic algorithm (GA) was applied to decrease the complexity of inhibition pathway to a few relevant molecular descriptors. Non-linear method (GA-SVM) showed to be better than the linear (GA-MLR) method in terms of the internal and the external prediction accuracy. The SVM model, with high statistical significance (R2train = 0.938; R2test = 0.870), was found to be useful for estimating the inhibition activity of 17ß-HSD3 inhibitors. The models were validated rigorously through leave-one-out cross-validation and several compounds as external test set. Furthermore, the external predictive power of the proposed model was examined by considering modified R2 and concordance correlation coefficient values, Golbraikh and Tropsha acceptable model criteria's, and an extra evaluation set from an external data set. Applicability domain of the linear model was carefully defined using Williams plot. Moreover, Euclidean based applicability domain was applied to define the chemical structural diversity of the evaluation set and training set.

Preconcentration and determination of 2-mercaptobenzimidazole by dispersive liquid-liquid microextraction and experimental design.

A method was developed to determine 2-mercaptobenzimidazole in water and urine samples using dispersive liquid-liquid microextraction technique coupled with ultraviolet-visible spectrophotometry. It was essential to peruse the effect of all parameters that can likely influence the performance of extraction. The influence of parameters, such as dispersive and extraction solvent volume and sample volume, on dispersive liquid-liquid microextraction was studied. The optimization was carried out by the central composite design method. The central composite design optimization method resulted in 1.10 mL dispersive solvent, 138.46 µL extraction solvent, and 4.46 mL sample volume. Under the optimal terms, the calibration curve was linear over the range of 0.003-0.18 and 0.007-0.18 µg/mL in water and urine samples, respectively. The limit of detection and quantification of the proposed approach for 2-mercaptobenzimidazole were 0.013 and 0.044 µg/mL in water samples and 0.016 and 0.052 µg/mL in urine samples, respectively. The method was successfully applied to determination of 2-mercaptobenzimidazole in urine and water samples.

Preconcentration and determination of ceftazidime in real samples using dispersive liquid-liquid microextraction and high-performance liquid chromatography with the aid of experimental design.

A rapid and simple method for the extraction and preconcentration of ceftazidime in aqueous samples has been developed using dispersive liquid-liquid microextraction followed by high-performance liquid chromatography analysis. The extraction parameters, such as the volume of extraction solvent and disperser solvent, salt effect, sample volume, centrifuge rate, centrifuge time, extraction time, and temperature in the dispersive liquid-liquid microextraction process, were studied and optimized with the experimental design methods. Firstly, for the preliminary screening of the parameters the taguchi design was used and then, the fractional factorial design was used for significant factors optimization. At the optimum conditions, the calibration curves for ceftazidime indicated good linearity over the range of 0.001-10 µg/mL with correlation coefficients higher than the 0.98, and the limits of detection were 0.13 and 0.17 ng/mL, for water and urine samples, respectively. The proposed method successfully employed to determine ceftazidime in water and urine samples and good agreement between the experimental data and predictive values has been achieved.

QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR).

Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r(2), concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.

Mixed hemimicelles solid-phase extraction based on sodium dodecyl sulfate (SDS)-coated nano-magnets for the spectrophotometric determination of Fingolomid in biological fluids.

In this study, mixed hemimicelles solid-phase extraction (SPE) based on sodium dodecyl sulfate (SDS)-coated nano-magnets Fe3O4 was investigated as a novel method for the separation and determination of Fingolimod (FLM) in water, urine and plasma samples prior to spectrophotometeric determination. Due to the high surface area of these new sorbents and the excellent adsorption capacity after surface modification by SDS, satisfactory extraction recoveries can be produced. The main factors affecting the adsolubilization of analysts, such as pH, surfactant and adsorbent amounts, ionic strength, extraction time and desorption conditions were studied and optimized. Under the selected conditions, FLM has been quantitatively extracted. The accuracy of the method was evaluated by recovery measurements on spiked samples, and good recoveries of 96%, 95% and 88% were observed for water, urine and plasma respectively. Proper linear behaviors over the investigated concentration ranges of 2-26, 2-17 and 2-13 mg/L with good coefficients of determination, 0.998, 0.997 and 0.995 were achieved for water, urine and plasma samples, respectively. To the best of our knowledge, this is the first time that a mixed hemimicelles SPE method based on magnetic separation and nanoparticles has been used as a simple and sensitive method for monitoring of FLM in water and biological samples.

2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA.

Two and Three-dimensional quantitative structure-activity relationship (2D, 3D-QSAR) study was performed for some pyrazole-thiazolinone derivatives as EGFR kinase inhibitors using the CoMFA, CoMSIA and GA-MLR methods. The utilized data set was split into training and test set based on hierarchical clustering technique. From the five CoMSIA descriptors, electrostatic field presented the highest correlation with the activity. The statistical parameters for the CoMFA (r(2)=0.862, q(2)=0.644) and CoMSIA (r(2)=0.851, q(2)=0.740) were obtained for the training set with the common substructure-based alignment. The obtained parameters indicated the superiority of the CoMSIA model over the CoMFA model. A test set consisted of seven compounds was used to evaluate the proposed models. The results of contour maps which were presented by each method lead to some insights for increasing the inhibition activity of compounds. The 2D-QSAR model was built based on three descriptors selected by genetic algorithm and showed high predictive ability (R(2) train= 0.843, Q(2) LOO=0.787). Molecular docking study was also performed to understand the type interactions presented in binding site of the receptor and ligand. The developed models in parallel with molecular docking can be employed to design and derive novel compounds with the potent EGFR inhibitory activity.

Analysis of B-Raf[Formula: see text] inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies.

In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-Raf[Formula: see text] inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results [Formula: see text], which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values [Formula: see text] based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained [Formula: see text]. The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf[Formula: see text] kinase.

3D-QSAR analysis of MCD inhibitors by CoMFA and CoMSIA.

Three-dimensional quantitative structure-activity relationship was developed for the series of compounds as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q(2)=0.558, r(2)=0.841) and CoMSIA (q(2)= 0.615, r(2) = 0.870) models were derived based on 38 compounds as training set in the basis of the selected alignment. The external predictive abilities of the built models were evaluated by using the test set of nine compounds. From obtained results, the CoMSIA method was found to have highly predictive capability in comparison with CoMFA method. Based on the given results by CoMSIA and CoMFA contour maps, some features that can enhance the activity of compounds as MCD antagonists were introduced and used to design new compounds with better inhibition activity.

An efficient piecewise linear model for predicting activity of caspase-3 inhibitors.

BACKGROUND AND PURPOSE OF THE STUDY: Multimodal distribution of descriptors makes it more difficult to fit a single global model to model the entire data set in quantitative structure activity relationship (QSAR) studies. METHODS: The linear (Multiple linear regression; MLR), non-linear (Artificial neural network; ANN), and an approach based on "Extended Classifier System in Function approximation" (XCSF) were applied herein to model the biological activity of 658 caspase-3 inhibitors. RESULTS: Various kinds of molecular descriptors were calculated to represent the molecular structures of the compounds. The original data set was partitioned into the training and test sets by the K-means classification method. Prediction error on the test data set indicated that the XCSF as a local model estimates caspase-3 inhibition activity, better than the global models such as MLR and ANN. The atom-centered fragment type CR2X2, electronegativity, polarizability, and atomic radius and also the lipophilicity of the molecule, were the main independent factors contributing to the caspase-3 inhibition activity. CONCLUSIONS: The results of this study may be exploited for further design of novel caspase-3 inhibitors.

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.

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.

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.