Enhanced Replacement Method Research Articles

Structure-based modeling of critical micelle concentration (CMC) of anionic surfactants in brine using intelligent methods

Critical micelle concentration (CMC) is one of the main physico-chemical properties of surface-active agents, also known as surfactants, with diverse theoretical and industrial applications. It is influenced by basic parameters such as temperature, pH, salinity, and the chemical structure of surfactants. Most studies have only estimated CMC at fixed conditions based on the surfactant’s chemical parameters. In the present study, we aimed to develop a set of novel and applicable models for estimating CMC of well-known anionic surfactants by considering both the molecular properties of surfactants and basic affecting factors such as salinity, pH, and temperature as modeling parameters. We employed the quantitative-structural property relationship technique to employ the molecular parameters of surfactant ions. We collected 488 CMC values from literature for 111 sodium-based anionic surfactants, including sulfate types, sulfonate, benzene sulfonate, sulfosuccinate, and polyoxyethylene sulfate. We computed 1410 optimized molecular descriptors for each surfactant using Dragon software to be utilized in the modelling processes. The enhanced replacement method was used for selecting the most effective descriptors for the CMC. A multivariate linear model and two non-linear models are the outputs of the present study. The non-linear models were produced using two robust machine learning approaches, stochastic gradient boosting (SGB) trees and genetic programming (GP). Statistical assessment showed highly applicable and acceptable accuracy of the newly developed models (RSGB2 = 0.999395 and RGP2 = 0.954946). The ultimate results showed the superiority and greater ability of the SGB method for making confident predictions.

Evaluation of the flammability characteristics of alkyl esters: New QSPR models

In the present study, new quantitative structure–property relation (QSPR) models have been developed to predict different flammability characteristics (i.e., Lower flammability temperature (LFLT), Upper flammability temperature (UFLT), Lower flammability percent (LFL(V%), and Upper flammability percent (UFL(V%)) of pure Alkyl esters. In this regard, new data sets containing 179 different alkyl esters from 10 different chemical categories were used. In the model development procedure, the appropriate molecular descriptors were selected for each property using the enhanced replacement method (ERM). Afterward, a multivariable linear model and three nonlinear models based on genetic programming (GP), random forest regression (RFR), and support vector regression (SVR) were developed using the molecular descriptors as input variables. The implementation of different internal and external validation methods confirmed the acceptable prediction capability of the developed models. In this regard, the average absolute error for the best models were 7.61 K for LFLT, 7.96 K for UFLT, 0.10 % for LFL (V%), and 1.10 for UFL(V%) over the whole dataset. A comparison between the prediction capabilities of the developed QSPR models with previous models also confirmed the superiority of the developed models in this study. Therefore, the developed QSPR models can be employed in the evaluation of the flammability characteristics of new alkyl esters such as new fatty acid alkyl esters in the biodiesel.

New structure-based models for the prediction of flash point and autoignition temperatures of alkyl esters

In this study, new models based on quantitative structure-property relationship (QSPR) have been proposed for the prediction of autoignition temperature (AIT) and flash point (FP) of different alkyl esters. In this regard, data sets containing 126 and 179 alkyl esters from 10 categories were used for AIT and FP, respectively. The enhanced replacement method (ERM) was applied for choosing the appropriate molecular descriptors, and linear models were developed based on the selected descriptors. Nonlinear models were also developed for AIT and FP using genetic programming (GP), support vector regression (SVR), and random forest regression (RFR). Then, the predictive performance of each model was evaluated through internal and external validation techniques considering several statistical parameters such as coefficient of determination (R2), root mean square error (RMSE), and percent of average absolute relative deviation (AARD). The outcome of the validation techniques confirmed the satisfactory agreement between the predicted and experimental data. The AARD%, R2, and RMSE for the best corresponding models (GP-based nonlinear models) was reported as 4.54%, 0.74, and 39.01 for AIT and 2.51%, 0.98, and 12.32 for FP, respectively. The superiority of the new models over the previous models was also proved by comparing the prediction capability of different models.

Investigation of enhanced exploitation of natural gas hydrate and CO2 sequestration combined gradual heat stimulation with CO2 replacement in sediments

CO2 replacement is a promising technology that simultaneously produces methane and carbon dioxide sequestration. In this work, a novel gradual heat injection enhanced replacement method (GHIR) was proposed to improve the methane production from natural gas hydrate sedimentary layer. The effects of heat injection mode on methane recovery, energy efficiency and the optimal operating conditions of GHIR were studied. The results have shown that GHIR enhances the CH4 recovery percentage from 8.0% to 25.4%. EROI could be further enhanced to 20.14. Because the thermal conductivity of hydrate is larger than the dissociation hydrate layer, the heat transfer analysis shown that the incremental heat injection enhanced replacement method (IHIR) is the best mode for gradual heat stimulation combined CO2 replacement. This work provides a novel method for future marine NGH development and promotes carbon sequestration by applying the CH4/CO2 replacement method.

Study on the electrically enhanced process for cadmium removal by a pulse in a sulfuric acid system

Aiming at the problems of low cadmium removal efficiency in zinc replacement and poor pressing plate effect of cadmium residues in the latter stage of the process, a novel electrically enhanced replacement method for cadmium removal based on a pulsed electric field was proposed. Comparative experiments with different current types verified the superiority of pulse current in the cadmium removal process to other known methods. At 45 °C, anode current density of 20 A·m−2, cathode-to-anode area ratio of 1:3, pulse duty ratio of 1:4, and pulse frequency of 500 Hz, the cadmium removal efficiency reached 96.80% in three hours. Furthermore, electrochemical test results for anodic reaction illustrated that the rise in solution acidity and Cd2+ concentration accelerated the replacement of zinc to cadmium. Correspondingly, increases in temperature, Cd2+ concentration, and solution pH inhibited the dissolution of the precipitated cadmium.

New empirical correlations for the prediction of critical properties and acentric factor of S-containing compounds

In the present study, simple empirical correlations have been developed to estimate the critical properties (i.e. TC, PC, and VC) and acentric factor (ω) of S-containing compounds. The variables of correlations are a set of simple parameters, including normal boiling point temperature (Tb), molecular weight (MW), and the number of atoms and bonds. A comprehensive dataset containing more than 130 S-containing compounds, including thiophenes, sulfides, mercaptans, siloxanes, and others, has been used for the model development. The parameter selection of the models has been carried out using the Enhanced Replacement Method. Two specific linear and non-linear models have been separately developed for each critical property and ω. The genetic programming method was applied for the development of the non-linear model. Statistical evaluation of the developed models confirmed the satisfactory capability of the models to predict the critical properties and ω of new compounds. Indeed, the values of the coefficient of determination (R2) of the non-linear models for TC, PC, VC, and ω were 0.9690, 0.9076, 0.9890, and 0.9467, respectively. In addition, the values of the average absolute relative deviation (AARD%) of the non-linear models for TC, PC, VC, and ω were 2.1677, 7.8375, 3.8919, and 9.9344, respectively.

Investigating fluorescence quenching of cysteine-functionalized carbon quantum dots by heavy metal ions: Experimental and QSPR studies

Carbon quantum dots (CQDs) have potential application as fluorescent probesfor the heavy metal detection. But there are few reports on the investigation of the quenching mechanism of CQDs by heavy metal ions. In this study, CQDs were prepared by directly pyrolyzing turmeric powder and functionalized using the thiol functionality of cysteine. Then the fluorescence quenching of cysteine-functionalized CQDs (Cys-CQDs) by 25 heavy metal ions were experimentally measured. Then the effect of the physical and chemical properties of heavy metal ions on the fluorescence quenching of CQDs was investigated by using the quantitative structure–property relationship (QSPR) method. Applying different machine learning method including genetic algorithm, enhanced replacement method, support vector machine, and multi linear regression, some reliable and predictive models were developed. Based on the variables included in the final QSPR model, covalent interactions of metal ions with functional groups on CQDs surface and the atomic environment were significant effect on the quenching of CQDs by heavy metal ions. Briefly, the tendency of metal ion to accept electrons from a donor ligand and the hard and soft acids and bases affinities can be considered as a primary factor governing the mechanism of quenching of CQDs by heavy metal ions.

QSAR analysis of the acetylcholinesterase inhibitory activity of some tertiary amine derivatives of cinnamic acid

Acetylcholinesterase (AChE) is an essential enzyme in the nervous system that increases the rate of the hydrolysis of the neurotransmitter acetylcholine. Excessive hydrolysis of acetylcholine is one of the factors that cause Alzheimer’s disease, and the inhibition of AChE might be one way to prevent and control the disease. In the present study, a QSAR study was developed on a set of 72 tertiary amine derivatives of cinnamic acids as a new family of acetylcholinesterase inhibitors. Enhanced replacement method (ERM) was used for variable selection, and multiple linear regression (MLR) was performed to correlate between structural features of molecules and their acetylcholinesterase inhibitory activity. The result suggests that the four variables, namely, HVcpx, VEA2, HATS5u, and qpmax indices, play an important role in the AChE inhibitory activities. The proposed model exhibited good statistical parameters for the training set (with R2train = 0.913, q2LOO = 0.896, F-ratio = 140.04) and test set (R2test = 0.856, $$ {r}_m^2 $$ =0.79, $$ {r}_m^{\prime 2}=0.66 $$ , $$ \overline{r_m^2}=0.72 $$ , and $$ {\Delta r}_m^2=0.13 $$ ).

Prediction of critical properties of sulfur-containing compounds: New QSPR models

In this study, new models have been proposed for the prediction of different critical properties (critical temperature (TC), critical pressure (PC), critical volume (VC), and acentric factor (ω)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R2) were 0.936, 0.976, 0.990, and 0.917 for TC, PC, VC, and ω, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded.

Prediction of toxicity of Ionic Liquids based on GC-COSMO method

In order to evaluate the toxicity of several different ionic liquids (ILs) towards the leukemia rat cell line (ICP-81), an efficient and reliable quantitative structure-activity relationships (QSAR) model is developed based on descriptors from COSMO-SAC (conductor-like screening model for segment activity coefficient) model. The distribution of screen charge density (σ-profile) of 127 ILs is calculated by GC-COSMO (group contribution based COSMO) method. Two segmentation methods toward σ-profile are used to find out the appropriate descriptors for the QSAR model. The optimal subset of descriptors is obtained by enhanced replacement method (ERM). A multiple linear regression (MLR) and multilayer perceptron technique (MLP) are used to build the linear and nonlinear models, respectively, and the applicability domain of the models is assessed by the Williams plot. It turns out that the nonlinear model based the second segmentation method (MLP-2) is the best QSAR model with an R2=0.975, MSE=0.019 for the training set and R2=0.938, MSE=0.037 for the test set. The reliability and robustness of the presented QSAR models are confirmed by Leave-One-Out (LOO) cross and external validations.

New structure-based models for the prediction of flash point of multi-component organic mixtures

In this study, new models based on quantitative structure–property relationship (QSPR) have been developed to estimate the flash point of multi-component mixtures. For the model development, a large data set including 921 experimental data on the flash point of 93 different binary mixtures including different chemical families was applied. Enhanced replacement method (ERM) was employed for subset variable selection. Based on the ERM selected molecular descriptors of mixture, two new models including linear model, and artificial neural network (ANN) based model have been proposed. The prediction capability of the developed models was evaluated using different statistical criteria. Besides, the estimation capability of the developed models was evaluated by estimating the flash point of 4 different ternary mixtures containing 221 experimental data as an extra external validation data set based on “mixture out” external validation strategy. All statistical analysis confirmed that the proposed models are reliable and accurate.

An exploratory study using QICAR models for prediction of adsorption capacity of multi-walled carbon nanotubes for heavy metal ions

ABSTRACTThe Quantitative Ion Character–Activity Relationship (QICAR) method was used for correlating metal ionic characteristics with the maximum adsorption capacity (qmax) of multi-walled carbon for heavy metals. The experimental values of qmax for 25 heavy metal ions, estimated by the Langmuir isotherm model, were used to construct a QICAR model. The genetic algorithm, enhanced replacement method and successive projection algorithm procedures were applied as variable selection algorithms to choose the optimal subsets of descriptors. The selected variables were correlated with qmax values by using partial least squares (PLS) regression. Orthogonal signal correction was applied as a pre-processing technique. Among of different variable selection methods, the enhanced replacement method displayed noticeable statistical parameters of the final model. The results of the enhancement replacement method–orthogonal correction signal–PLS model, with RMSEC = 0.733, r2c = 0.999 and r2p = 0.946, were excellent and dramatically better than those of other models. The developed QICAR model satisfied the internal and external validation criteria. The importance of electronegativity, ionic radius and atomic number of the heavy metal ions indicated the impact of the tendency to accept electrons and the size of ions in adsorption on carbon nanotubes.

Quantitative structure–activity relationship study using genetic algorithm–enhanced replacement method combined with molecular docking studies of isatin derivatives as inhibitors of human transglutaminase 2

Inhibition of the enzyme human transglutaminase 2 (TG2) is important for the treatment of various diseases. Isatin derivatives, including 3‐acylidene‐2‐oxoindoles, inhibit of human TG2. In this work, quantitative structure–activity relationship (QSAR) and molecular docking studies were performed on the inhibitory activity of isatin derivatives. First, genetic algorithm (GA) was used for selecting the sum of descriptors. Then a small number of descriptors were selected using the enhanced replacement method (ERM) and the stepwise (SW) method. The descriptors nCl, HE, R4m+, and n = CHR were selected using GA‐ERM and the descriptors R6m, R5e +, n = CHR, and HE were produced using the GA‐SW method. Two linear and nonlinear methods, namely multiple linear regression (MLR) and support vector regression (SVR), were used for QSAR modeling. GA‐ERM outperformed in combination with the SVR method, which indicated more power at the prediction of inhibitory activity. Also, molecular docking studies were performed for understanding the mechanism of interaction of isatin derivatives with TG2 and investigating the effect of some descriptors such as the hydration energy (HE) on the inhibitory activity of the studied compounds. Docking results revealed that the formation of hydrogen bonding and hydrophobic interactions play important roles in the connection between the protein TG2 (PDB: 3S3P) and isatin derivatives.

Modeling and predicting the solute polarity parameter in reversed-phase liquid chromatography using quantitative structure-property relationship approaches.

A prediction of quantitative structure-property relationships is developed to model the polarity parameter of a set of 146 organic compounds in acetonitrile in reversed-phase liquid chromatography. Enhanced replacement method and support vector machine regressions were employed to build prediction models based on molecular descriptors calculated from the structure alone. The correlation coefficients between experimental and predicted values of polarity parameter for the test set by enhanced replacement method and support vector machine were 0.970 and 0.993, respectively. The obtained results demonstrated that the support vector machine model is more reliable and has a better prediction performance than the enhanced replacement method.

QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine

A quantitative structure property relationship (QSPR) study based on enhanced replacement method (ERM) and support vector machine (SVM) was used to correlate molecular structures to their bovine serum albumin water partition coefficients (KBSA/W). A wide variety of natural organic compounds and drugs were selected as a dataset and suitable sets of molecular descriptors were calculated using Dragon package. ERM was used as variable selection method. The nonlinear-support vector machine models were applied to correlate the ERM-selected molecular descriptors with the experimental values of KBSA/W. Results obtained demonstrate the reliability and good predictability of support vector machine model to predict KBSA/W of organic compounds and drugs. Satisfactory results demonstrate that the ERM approach is a very powerful method for variable selection and the predictive ability of the SVM model is superior to those acquired by ERM.

Integrated Application of Enhanced Replacement Method and Ensemble Learning for the Prediction of BCRP/ABCG2 Substrates

Background: Breast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific effluxtransporter which belongs to the ATP-binding Cassette superfamily. Up-regulation of BCRP is associated to multi-drug resistance in a number of conditions, e.g. cancer and epilepsy. Recent proteomic studies show that high expression levels of BCRP are found in healthy human intestine and at the blood-brain barrier, limiting the absorption and brain distribution of its substrates. Therefore, the early recognition of BCRP substrates seems to be crucial in the early phase of drug discovery. Keywords: Breast Cancer Resistance Protein, ABC transporters, ABCG2, enhanced replacement method, ensemble learning, linear classifiers.

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method

In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure–property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r2, between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.

Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models

Quantitative structure–activity relationship (QSAR) studies were performed in this work to predict the pIC50 of non-glycoside sodium-dependent glucose cotransporter-2 (SGLT2) inhibitors (46 triazole derivatives). Four descriptors were selected from the pool of DRAGON descriptors using the enhanced replacement method. Three nonlinear regression methods—multilayer perceptron neural network (MLP NN), support vector machine (SVM), and projection pursuit regression (PPR)—were then used to build the QSAR models. The performance of the obtained models was assessed through the cross-validation and external validation of the test set. PPR produced a better model than MLP NN and SVM with coefficients of determination of 0.962 and 0.871 and root mean square errors of 0.162 and 0.471 for the training and test sets, respectively. This study developed simple and efficient approaches to predict the pIC50 of triazole derivatives and provided some insights into their structural features for the development of more potential SGLT2 inhibitors.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure–property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation.

A new structure-based model for estimation of true critical volume of multi-component mixtures

In this study, a new quantitative structure–property relationship (QSPR) has been proposed to estimate true vapor–liquid critical volume of multi-component mixtures. In developing this model, 598 experimental data on true critical volume for 110 different binary mixtures were applied. The mixture molecular descriptors were computed according to the molecular descriptors of pure components involved in the mixture and their molar fractions. Enhanced replacement method (ERM), as an effective tool for subset variable selection, was utilized. The proposed model is simple multivariate linear equations with six variables. The prediction capability of the proposed model for different families of compounds was critically analyzed. Besides, the capability of the proposed model has been tested by predicting true critical volume of 7 different multi-component mixtures containing 40 experimental data points. The average absolute relative deviation of the proposed QSPR model over all experimental data is 9.7% and 8.7% for binary mixtures and multi-component mixtures, respectively.