Quantitative Structure-mobility Relationship Research Articles

Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions.

The ligands of the imidazoline and α-adrenergic receptors are mainly imidazoline and guanidine derivatives, known as centrally-acting antihypertensives and compounds with potential use in various neurological disorders. The extent of their ionisation has a major influence on their behaviour in the different analytical systems. The main objective of this work was to compare the mechanism of chromatographic retention and electrophoretic mobility under acidic, neutral and basic conditions. Multiple Linear Regression and Partial Least Squares Regression were applied for the QSRR (quantitative structure-retention relationship) and QSMR (quantitative structure-mobility relationship) modelling and to select the most important molecular parameters describing the chromatographic and electrophoretic behaviour of the investigated compounds. The most important molecular descriptors, such as the chemical composition of the compounds, lipophilicity, polarizability and molecular branching, in the selected QSRR models showed that an important insight into the retention behaviour can be derived from the 0D-, 1D- and 2D-descriptors. The electrophoretic mobility could be explained by 2D- and 3D-descriptors, which provide information on the molecular mass, size and complexity, as well as on the influence of charge transfer and electronic properties on the migration behaviour. All created QSRR/QSMR models met the stringent validation criteria and showed high potential in describing the chromatographic and electrophoretic behaviour of investigated compounds.

A quantitative structure–mobility relationship of organic acids using solvation parameters

ABSTRACTA quantitative structure–mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN–QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R2) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N = 5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism.

Quantitative structure-mobility relationship analysis of imidazoline receptor ligands in CDs-mediated CE

The performed quantitative structure-mobility relationship (QSMR) study has investigated relative migration times of 11 guanidine/imidazoline derivatives, imidazoline receptor ligands, in CE system containing one of CDs, α-, β-, or γ-CD, using linear and nonlinear modeling methods. The analyzed ligands and their inclusion complexes with CDs were fully examined and optimized at semiempirical parametrized model 3 level. The density functional theory, such as B3LYP/6-31G+(d,p)/3-21G(d)/STO-3G(d,p)/STO-3G(d), and ab initio theory, such as HF/3-21G(d)/STO-3G(d), were applied for molecular descriptors computation of the optimized ligands and their complexes. Predictive performances of the developed QSMR models were tested by use of the cross-validation and external test set prediction. Obtained results for Q(2) values (0.869, 0.911, and 0.966 for CE system containing α-, β-, and γ-CD, respectively) and root mean squared error of prediction (0.239, 0.242, and 0.288 for α-, β-, and γ-CD, respectively) were proved high predictive power of the proposed models. Finally, multitarget QSMR model, using the ligands descriptors (X) and the relative migration time in presence of α-CD (Y1), β-CD (Y2), and γ-CD (Y3), has been created. The multitarget QSMR model can be used as initial screening predictive tool for CE migration behavior of other related guanidine/imidazoline derivatives in presence of α-, β-, and γ-CD.

Prediction of Electrophoretic Mobilities of Organic Acids Using Artificial Neural Networks with Three Different Training Functions

The quantitative structure–mobility relationship study has been done to develop the correlation between the electrophoretic mobility of a number of common organic acids in capillary electrophoresis and their molecular structures. Molecular descriptors calculated from structure, were used to represent molecular structures. A subset of the calculated descriptors was used in model development. Multiple linear regression and artificial neural networks are utilized to construct the linear and non-linear prediction models. This paper focuses on investigating the role of weight update function in artificial neural networks. Therefore, artificial neural networks with three different weight update functions including Levenberg–Marquardt algorithm, gradient descent with variable learning rate back propagation and resilient back propagation were trained. Finally, obtained results using three different artificial neural networks have been compared.

Quantitative structure–mobility relationship studies of dipeptides in capillary zone electrophoresis using three-dimensional holographic vector of atomic interaction field

To explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of dipeptides in capillary zone electrophoresis (CZE), a stepwise multiple regression-multiple linear regression (SMR-MLR) model has been built. The dataset consists of electrophoretic mobilities, measured at pH 2.5, for 53 dipeptides. Among the existing empirical models, the Offord model (i.e., μ = Q/M 2/3) gives the best correlation for the dataset. A quantitative structure–mobility relationship (QSMR) has been developed using the Offord's charge-over-mass term as one descriptor combined with the three-dimensional holographic vector of atomic interaction field descriptors to account for the steric, hydrophobic and electrostatic interactions of the amino acid side chains. The MLR results of the dataset show an improvement in the predictive ability of the model over the simple Offord's relationship.

Quantitative structure–mobility relationship study of a diverse set of organic acids using classification and regression trees and adaptive neuro‐fuzzy inference systems

A quantitative structure-mobility relationship was developed to accurately predict the electrophoretic mobility of organic acids. The absolute electrophoretic mobilities (mu(0)) of a diverse dataset consisting of 115 carboxylic and sulfonic acids were investigated. A set of 1195 zero- to three-dimensional descriptors representing various structural characteristics was calculated for each molecule in the dataset. Classification and regression trees were successfully used as a descriptor selection method. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system. The root mean square errors for the calibration and prediction sets are 1.61 and 2.27, respectively, compared with 3.60 and 3.93, obtained from a previous mechanistic model.

Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis

Three machine learning techniques including back propagation artificial neural network (BP-ANN), radial basis function artificial neural network (RBF-ANN) and support vector regression (SVR) were applied to predicting the peptide mobility in capillary zone electrophoresis through the development of quantitative structure–mobility relationship (QSMR) models. A data set containing 102 peptides with a large range of size, charge and hydrophobicity was used as a typical study. The optimal modeling parameters of the models were determined by grid-searching approach using 10-fold cross-validation. The predicted results were compared with that obtained by the multiple linear regression (MLR) method. The results showed that the relative standard errors (R.S.E.) of the developed models for the test set obtained by MLR, BP-ANN, RBF-ANN and SVR were 11.21%, 7.47%, 5.79% and 5.75%, respectively, while the R.S.E.s for the external validation set were 11.18%, 7.87%, 7.54% and 7.18%, respectively. The better generalization ability of the QSMR models developed by machine learning techniques over MLR was exactly presented. It was shown that the machine learning techniques were effective for developing the accurate and relaible QSMR models.

A simple approach to the prediction of electrophoretic mobilities of diverse organic acids

Based on the principle of separation by capillary electrophoresis, three simple molecular structural descriptors, the number of acid groups (NG), number of atoms (NA) and molecular weight (MW) were employed to model the molecular structure–electrophoretic mobility relationship with the general form of μ 0 = aNG/( bNA k + cMW k ), where the value of k ranges from 1/3 to 2/3 depending on the magnitude of molecular weight. The absolute electrophoretic mobilities ( μ 0) of three diverse data sets including carboxylic acids, sulfonic acids and peptides were investigated. Each data set was randomly divided into a training set for model generation and a test set for prediction test. The absolute average relative deviations (AARD and AARD P) of the training set and the test set are 7.21% and 7.53% for data set 1, 1.99% and 1.96% for data set 2, 3.25% and 3.95% for data set 3, respectively. Compared to the quantitative structure–mobility relationship (QSMR) models reported in the literature, the approach proposed in this study is much simpler in terms of the calculation of structural descriptors and the interpretation of the physicochemical meaning of the models.

Study of quantitative structure–mobility relationship of the peptides based on the structural descriptors and support vector machines

Support vector machines (SVM), as a novel learning machine, was used to develop the non-linear quantitative structure–mobility relationship model of the peptides based on the calculated descriptors for the first time. The molecular descriptors representing the structural features of the compounds included the constitutional and topological descriptors calculated by CODESSA program, which can be obtained easily without optimizing the structure of the molecule, and CPSA (charged partial surface area) descriptors obtained by SYBYL software. The MLR method was used to select the descriptors responsible for the electrophoretic mobility of peptides and develop the linear model. The prediction result of the SVM model ( ɛ = 0.04, γ = 0.002 and C = 100) is much better than that obtained by MLR method. The RMS error of the training set, the test set and the whole set is 0.0569, 0.0553, 0.0565 and the prediction correlation coefficient is 0.925, 0.912 and 0.922, respectively. The prediction results are in agreement with the experimental values. This paper provided a new and effective method for predicting the electrophoretic behavior of peptide and some insight into what structural features are related to the electrophoretic mobility of peptides. Moreover, it also offered an idea about dealing with the structural optimization and obtaining their structural descriptors for biomacromolecules.

Development of migration models for acids in capillary electrophoresis using heuristic and radial basis function neural network methods

A quantitative structure-mobility relationship (QSMR) was developed for the absolute mobilities of a diverse set of 277 organic and inorganic acids in capillary electrophoresis based on the descriptors calculated from the structure alone. The heuristic method (HM) and the radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear prediction models, respectively. The prediction results were in agreement with the experimental values. The HM model gave a root-mean-square (RMS) error of 3.66 electrophoretic mobility units for the training set, 4.67 for the test set, and 3.88 for the whole data set, while the RBFNN gave an RMS error of 2.49, 3.19, and 2.65, respectively. The heuristic linear model could give some insights into the factors that are likely to govern the mobilities of the compounds, however, the prediction results of the RBFNN model seem to be better than that of the HM.

Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

The aim of this work was to explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of small peptides in capillary zone electrophoresis (CZE). The data set consists of electrophoretic mobilities, measured at pH 2.5, for 125 peptides ranging in size between 2 and 14 amino acids. Among the existing empirical models, the Offord model (i.e., mu identical with Q/M(2/3)) gave the best correlation for the data set. A quantitative structure-mobility relationship (QSMR) was developed using the Offord's charge-over-mass term (Q/M(2/3)) as one descriptor combined with the corrected steric substituent constant (E(s, c)) and molar refractivity (MR) descriptors to account for the steric effects and bulkiness of the amino acid side chains. The multilinear regression (MLR) of the data set showed an improvement in the predictive ability of the model over the simple Offord's relationship. A 3-4-1 back propagation artificial neural networks (BP-ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. The improved correlations by the BP-ANN analysis suggest the existence of nonlinear characteristic in the mobility-charge relationships.

Study of quantitative structure–mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis

Study of quantitative structure–mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis

Study of quantitative structure–mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis

A quantitative structure–mobility relationship (QSMR) was developed for the absolute mobilities of 115 carboxylic and sulphonic acids in capillary electrophoresis based on the descriptors calculated from the structure alone. The heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear prediction models, respectively. The prediction results were in agreement with the experimental values. The HM model gave an root-mean-square (RMS) error of 3.76 electrophoretic mobility units for the training set, 5.59 for the test set, and 4.19 for the whole data set, while the RBFNN gave an RMS error of 1.78, 2.04, and 1.83, respectively. The heuristic linear model could give some insights into the factors that are likely to govern the mobilities of the compounds, however, the prediction results of the RBFNN model seem to be better than that of the heuristic method.

Study of the Quantitative Structure‐Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines.

The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (*10-5 cm2 S-1 V-1), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobil...

Study of the quantitative structure-mobility relationship of carboxylic acids in capillary electrophoresis based on support vector machines.

The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (10(-5) cm(2) S(-1) V(-1)), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobility of aliphatic and aromatic carboxylic acids.

Study on the quantitative relationship between the structures and electrophoretic mobilities of flavonoids in micellar electrokinetic capillary chromatography

Quantitative structure–mobility relationship (QSMR) models were developed between the structures of flavonoids and their eletrophoretic mobilities in micellar electrokinetic capillary chromatography. Molecular descriptors calculated from structure alone are used to represent molecular structures, moreover, N t was defined by ourselves. Multiple linear regression and radial basis function neural networks (RBFNNs) are utilized to construct the linear and nonlinear prediction model, respectively. The optimal QSMR model developed was based on a 3-10-1 RBFNNs architecture. The root mean square errors in mobilities predictions for the data set was 0.1083 mobility unit (10 −4 cm 2 V −1 s −1). The prediction results were in good agreement with the experimental values.

Quantitative structure-mobility relationship modelling of electrokinetic chromatography of metal complexes: approaches and limitations.

A charged surfactant or an ionic polymer added to the capillary electrolyte introduces micellar solubilization and ion-exchange interactions, respectively, as a supplementary separation principle for metal complexes among a great many other analytes. Acting as a pseudostationary phase, such electrolyte additives make the separation mechanism fairly different from that based only on differences in electrophoretic mobility. A range of quantitative structure-mobility relationships were developed to explain the migration behavior of metal complexes in micellar and ion-exchange electrokinetic chromatographic systems as a function of their primary structural parameters. The validity of migration models tested using a comprehensive selection of experimental data available in the literature was shown to depend significantly on the judicious choosing of the analyte structural descriptors and on the dominance of either of the electrophoretic mobility of the analyte, partitioning into the micelle, and the ion-exchange interaction with the polymer. In the systems where the separation mechanism is dominated by neither chromatography nor electrophoresis, their relative contributions were accounted for by the degree of correlation between experimental and calculated mobilities and by variations in the absolute value and sign of regression coefficients at the most influencing parameters.