Dual-template imprinted polymer electrochemical sensor for simultaneous determination of malathion and carbendazim using graphene quantum dots

Malathion (MAL) and carbendazim (CBZ) are organophosphate pesticides and fungicides, respectively. They are often used simultaneously in agriculture, and both have been shown to have harmful effects on humans and animals. Therefore, it is important to be able to measure both of these toxins simultaneously in order to assess their potential risks. This study aims to design a dual template electrochemical sensor using a cost-effective graphite-epoxy composite electrode (GECE) modified with molecularly imprinted polymers (MIPs) coated on graphene quantum dots (GQDs) for simultaneous detection of MAL and CBZ in real samples. GQDs were synthesized initially, and their surface was coated with MIPs that were formed using MAL and CBZ as the template molecules, ethylene glycol dimethyl acrylate as the cross-linker, and methacrylic acid as the functional monomer. The GQDs@MIP were characterized using Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, and X-ray scattering spectroscopy. Parameters affecting the sensor response, such as the percentage of GQDs@MIP in the fabricated electrode, the pH of the rebinding solution and analysis solution, and the incubation time, were optimized. The optimum pH values of the rebinding solution were verified using density functional theory (DFT) calculations. Under the optimized conditions, differential pulse voltammetry (DPV) response calibration curves of MAL and CBZ were generated, and the results showed that the sensor had a linear response to MAL in the range of 0.02-55.00 μM with a limit of detection (LOD) of 2 nM (S/N = 3) and to CBZ in the range of 0.02-45.00 μM with a low LOD of 1 nM (S/N = 3). The results also demonstrated the proposed sensor's long-term stability and anti-interference capability. The practical applicability of the fabricated electrode was evaluated for real sample analysis, and good recovery values were obtained.

Research and study of 2-((4,6 dimethyl pyrimidine-2-yle) thio)-N-phenyl acetamide derivatives as inhibitors of sirtuin 2 protein for the treatment of cancer using QSAR, molecular docking and molecular dynamic simulation

Phenyl acetamide derivatives have a wide range of biological activities, so their research and development can be useful and effective for the design production of new drugs. In this project, quantitative structure-activity relationship (QSAR) was performed. For modeling two methods of multiple linear regression (MLR) and nonlinear regression of support vector machine (SVR) were used. In the MLR stage, the best model with the values of R²_train = 0.913 and R²_test = 0.881 was selected by stepwise method. In this model, 4 descriptors of BELV2, GATS8p, GATS6e and RDF080m were included, which were used as input for the nonlinear support vector regression method. In the SVR model, the best results were obtained using the radial Gaussian kernel function (RBF) with R²_train = 0.978 and R²_test = 0.990. In the next step, using molecular docking and molecular dynamic simulation methods, the interaction between phenyl acetamide derivatives and the sirtuin 2 protein was investigated. Examining the results of molecular docking, it was observed that these derivatives formed complexes by forming hydrogen and hydrophobic bonds with the sirtuin 2 protein. Also, the results of molecular dynamic simulation show that phenyl acetamide compounds form stable complex with the sirtuin 2 protein, and it was found that the compounds with more activity have formed a number of hydrogen bonds with the protein.

Fabrication of platinum-doped NiCo2O4 nanograss modified electrode for determination of carbendazim

In this study, a platinum-doped nickel cobaltite nanograss (Pt-doped NiCo₂O₄ NG) with its own unique structural features was initially synthesized, utilizing a simple hydrothermal method and then applied as a novel platform for the detection of carbendazim (C₉H₉N₃O₂; CBZ). To this end, the CBZ electrochemical signals were evaluated by means of differential pulse voltammetry (DPV), demonstrating the acceptable catalytic effect of the Pt-doped NiCo₂O₄ NG/screen-printed electrode (SPE) on the CBZ oxidation signal. Under the optimized conditions, CBZ was subsequently quantified by the Pt-doped NiCo₂O₄ NG/SPE with a wide linear range (0.03-140 μM) and a low limit of detection (LOD) value (0.005 μM). The proposed sensor was thus characterized by good anti-interference ability, selectivity, and stability. The analysis of the real samples, viz. tomato and lettuce, also confirmed that the given sensor had good recoveries and relative standard deviation (RSD). Ultimately, a comparison between liquid chromatography-mass spectrometry (LC-MS) and this method established no significant difference in the results.

A new electrochemical DNA biosensor for determination of anti-cancer drug chlorambucil based on a polypyrrole/flower-like platinum/NiCo2O4/pencil graphite electrode

In the current study, DNA immobilization was performed on pencil graphite (PG) modified with a polypyrrole (PPy) and flower-like Pt/NiCo₂O₄ (FL-Pt/NiCo₂O₄) nanocomposite, as a new sensitive electrode to detect chlorambucil (CHB). Energy dispersive X-ray (EDX) analysis, X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques were employed to characterize the synthesized FL-Pt/NiCo₂O₄ and PPy/FL-Pt/NiCo₂O₄ nanocomposites. Moreover, differential pulse voltammetry (DPV) was selected to assess the guanine and adenine electrochemical responses on the DNA sensor. The CHB determination was performed using the maximum currents towards adenine and guanine in the acetate buffer solution (ABS). According to the results, ds-DNA/PPy/FL-Pt/NiCo₂O₄/PGE was able to detect the different concentrations of CHB in the range between 0.018 and 200 μM, with a detection limit of (LOD) of 4.0 nM. The new biosensor was also exploited for CHB determination in real samples (serum, urine and drug), the results of which revealed excellent recoveries (97.5% to 103.8%). Furthermore, the interaction between ds-DNA and CHB was studied using electrochemistry, spectrophotometry and docking whose outputs confirmed their effective interaction.

In the current study, DNA immobilization was performed on pencil graphite (PG) modified with a polypyrrole (PPy) and flower-like Pt/NiCo₂O₄ (FL-Pt/NiCo₂O₄) nanocomposite, as a new sensitive electrode to detect chlorambucil (CHB).

In silico study combining QSAR, docking and molecular dynamics simulation on 2,4-disubstituted pyridopyrimidine derivatives

2,4-Disubstituted pyridopyrimidine derivatives were studied against ABCG2 enzyme. The modeling of pyridopyrimidine derivatives were done using two methods of multiple linear regression and support vector regression and four molecular descriptors of BIC4, log p, VRA2, and binding energy were selected for modeling. The statistical results were satisfactory. The interactions of ABCG2 enzyme with pyridopyrimidine derivatives were investigated using molecular docking method. Based on the results, increasing of binding energy and hydrophobicity of the compounds increase their inhibitory activity. Protein stability in complex with pharmaceutical derivatives was discussed using molecular dynamics simulation method.

Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives

A quantitative structure−retention relationship study has been carried out on the retention times of 63 furan and phenol derivatives using artificial neural networks (ANNs). First, a large number of descriptors were calculated using HyperChem, Mopac, and Dragon softwares. Then, a suitable number of these descriptors were selected using a multiple linear regression technique. This paper focuses on investigating the role of weight update functions in developing ANNs. Therefore, selected descriptors were used as inputs for ANNs with six different weight update functions including the Levenberg−Marquardt back-propagation network, scaled conjugate gradient back-propagation network, conjugate gradient back-propagation with Powell−Beale restarts network, one-step secant back-propagation network, resilient back-propagation network, and gradient descent with momentum back-propagation network. Comparison of the results indicates that the Levenberg−Marquardt back-propagation network has better predictive power than the other methods.

Application of multivariate image analysis in modeling (13) C-NMR chemical shifts of mono substituted pyridines

Multivariate image analysis (MIA) descriptors have been applied to the quantitative structure-property relationship (QSPR) study of (13) C-NMR chemical shifts of 2-mono substituted pyridines. In this method, descriptors are generated from pixels of images and are analyzed with different multivariate methods. Correlation ranking-principal component regression and correlation ranking-principal component-artificial neural networks were applied in constructing predictor models. In this article, the role of weight update function in artificial neural networks was investigated too. Obtained results using the correlation ranking-principal component-artificial neural network method showed high performance for predicting of (13) C-NMR chemical shifts of pyridine derivatives. Also, these results indicated that MIA descriptors may be useful to predict (13) C-NMR chemical shifts. Finally, The MIA-QSPR approach coupled to artificial neural networks revealed that the predictive ability of MIA descriptors is comparable or even superior for the pyridine derivatives when compared with the ChemDraw program or gauge included atomic orbital procedure for (13) C chemical shifts calculations.

Modeling of 13C NMR chemical shifts of benzene derivatives using the RC–PC–ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors

The primary goal of a quantitative structure–property relationship study is to identify a set of structurally based numerical descriptors that can be mathematically linked to a property of interest. In this work, two main groups of descriptors have been used to predict 13C NMR chemical shifts of ipso, ortho, meta, and para positions in a series of 113 monosubstituted benzenes. First, two groups of descriptors — original molecular descriptors (constitutional, topological, electronic, and geometrical) and multivariate image analysis (MIA) descriptors — were calculated. Then, calculated descriptors were subjected to principal component analysis and the most significant principal components were extracted. Finally, more correlated principal components were used as inputs of artificial neural networks. The results obtained using the rank correlation–principal component–artificial neural network (RC–PC–ANN) modeling method show high ability to predict 13C NMR chemical shifts. Also, comparison of the results indicates that MIA descriptors show better ability to predict 13C NMR chemical shifts than the original molecular descriptors.

Quantitative structure-retention relationship study of some phenol derivatives in gas chromatography

A quantitative structure-retention relationship (QSRR) model has been developed for the gas chromatographic retention times of 37 phenolic derivatives in a DB-5 non-polar column (95% dimethyl and 5% diphenyl-polysiloxane). As a first step, multiple linear regression (MLR) was employed to gain informative descriptors that can predict the retention times of these compounds. Descriptors appearing in the MLR model are categorized as topological and geometric parameters that comply with the applied column. Furthermore, each molecular descriptor in this model was examined to unfold the relationship between molecular structures and their retention times. Then, a 4-4-1 neural network was developed using the descriptors selected by the MLR model. The comparison of the standard errors and correlation coefficients reveals the superiority of artificial neural networks (ANN) over the MLR model. This refers to the fact that the retention behaviors of molecules display non-linear characteristics. The consistency and reliability of ANN model was investigated using the L4O cross-validation technique. The obtained results are closely in compliance with the experiment. Moreover, the mean effect of descriptors shows that Kier symmetry index is the most important factor affecting the retention behavior of molecules.

Prediction of gas chromatographic retention indices of a diverse set of toxicologically relevant compounds

For a set of 846 organic compounds, relevant in forensic analytical chemistry, with highly diverse chemical structures, the gas chromatographic Kovats retention indices have been quantitatively modeled by using a large set of molecular descriptors generated by software Dragon. Best and very similar performances for prediction have been obtained by a partial least squares regression (PLS) model using all considered 529 descriptors, and a multiple linear regression (MLR) model using only 15 descriptors obtained by a stepwise feature selection. The standard deviations of the prediction errors (SEP), were estimated in four experiments with differently distributed training and prediction sets. For the best models SEP is about 80 retention index units, corresponding to 2.1-7.2% of the covered retention index interval of 1110-3870. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups are well covered by the selected 15 descriptors. The developed models support the identification of substances in forensic analytical work by GC-MS in cases the retention data for candidate structures are not available.

Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks

The electrophoretic mobilities of 31 isomeric alkyl- and alkenylpyridines in capillary electrophoresis were predicted using an artificial neural network (ANN). The multiple linear regression (MLR) technique was used to select the descriptors as inputs for the artificial neural network. The neural network is a fully connected back-propagation model with a 3-6-1 architecture. The results obtained using the neural network were compared with those obtained using the MLR technique. Standard error of training and standard error of prediction were 6.28 and 5.11%, respectively, for the MLR model and 1.03 and 1.20%, respectively, for the ANN model. Two geometric parameters and one electronic descriptor that were used as inputs in the ANN are able to distinguish between the isomers.

Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks

The relative response factors (RRFs) of a flame ionization detection (FID) system and two pulsed discharge photoionization detection (PID) systems with different discharge gases are predicted for a set of organic compounds containing various functional groups. As a first step, numerical descriptors were calculated based on the molecular structures of compounds. Then, multiple linear regression (MLR) was employed to find informative subsets of descriptors that can predict the RRFs of these compounds. The selected MLR model for the FID system includes seven descriptors and two selected MLR models for the PID systems with argon- and krypton-doped helium as the discharge gases, respectively, include six and five descriptors. The descriptors appearing in the MLR models were considered as inputs for the self-training artificial neural networks (STANNs). A 7-7-1 STANN was generated for prediction of RRFs of the FID system, and two STANNs with the topologies of 6-7-1 and 5-6-1 were generated for the two PID systems. Comparison of the results indicates the superiority of neural networks over that of the MLR method. This is due to the nonlinear behaviors of relative response factors for all type of detectors studied in this work.

Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds

A quantitative structure-activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 13 different classes of organic compounds. The five descriptors appearing in the selected MLR model are molecular density, Winer number, boiling point, polarizability and square of polarizability. A 5-6-1 ANN and a 5-4-1 STANN were generated using the five descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR model. This is due to the fact that the retention behaviors of molecules show non-linear characteristics. Inspection of the results of STANN and ANN shows there are few differences between these methods. However, optimization of STANN is much faster and the number of adjustable parameters for this technique is much less compared with those of the conventional ANN.

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.