Linear Quantitative Structure-property Relationship Models Research Articles

Threshold Sooting Index of Sustainable Aviation Fuel Candidates from Composition Input Alone: Progress toward Uncertainty Quantification

A quantitative structure–property relationship model has been developed to predict the threshold sooting index (TSI) of arbitrary mixtures of aliphatic and aromatic hydrocarbons of known composition. The model employs contributions from eight molecular fragments plus a global shift and a penalty factor for naphthenic compounds. For each coefficient, the contributions were determined by a constrained regression to data from five different experimental campaigns, which were stitched together by setting the TSI of methylcyclohexane to 5 and the TSI of 1-methylnaphthalene to 100. Unique to this study; the TSI of 1,3,5-trimethylbenzene was restricted to the range of 54.7 and 63.1, which significantly constrains uncertainty. Within the composite training dataset, which contained 65 molecules and 124 data points, including simple mixtures, the model was found to match 95% of the data within 8.9 TSI. Validation of the model against n-butylcyclohexane, dimethylcyclooctane, and a six-component surrogate jet fuel shows prediction to be well within the 95-percentile confidence band of the experiment. This model is the first to integrate the linear blending rule for the TSI with a linear quantitative structure–property relationship model for the TSI, and the first time that referee controls have been applied to ensure that all datasets, experimental and modeled, are normalized to the same scale.

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method.

Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure–property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.

Exploring beneficial structural features of ionic surfactants for wettability alteration of carbonate rocks using QSPR modeling technique

Surfactant induced wettability alteration of reservoir rocks is an efficient process that enhances oil recovery of carbonate reservoirs. Finding a suitable surfactant for such a process is an interesting challenge in petroleum industry. To this purpose, generating a database of surfactant properties would be a good idea but property measurement of hundreds of surfactants and then screening them is a highly time consuming and expensive method. Therefore, the application of a mathematical model for prediction of surfactant properties may well be so helpful for our purpose. Quantitative structure-property relationship (QSPR) is a mathematical model that relates a specific property of materials to their structural characteristics. For the sake of QSPR modeling a dataset of 24 structurally different surfactants was created through the measurement of contact angles (as the most important surfactant characteristic in the wettability alteration process). Contact angle measurement was carried out through imaging a drop of n-decane as the model oil resting on a carbonate rock which has spent 2days immersed in surfactant solution. For this dataset a linear QSPR model of oil contact angle on carbonate rocks was derived using genetic algorithm based on multi-linear regression (GA-MLR) and verified by internal and external validation metrics. From a descriptive point of view, descriptors of the proposed model have a mechanistic interpretation and give an insight to the desired structural features enhancing wettability alteration ability of surfactants.

Prediction of refractive indices of ionic liquids – A quantitative structure-property relationship based model

In this communication, a reliable Quantitative Structure−Property Relationship (QSPR) model is developed to predict the refractive indices, nD, of ionic liquids at different temperatures. A dataset comprising 931 experimental data values of refractive index (λ = 589 nm) for 97 ionic liquids (extracted from the NIST Standard Reference Database) was used to develop and evaluate the model (80% of the data used as a training set and 20% as a test set). In this study, the effects of both anions and cations are considered in the development of the model. Genetic function approximation (GFA) is applied to select the model parameters (molecular descriptors) and develop a linear QSPR model. Statistical analysis of the performance of the model with respect to the dataset indicates an average absolute relative deviation (AARD%) of 0.51, a coefficient of determination (R2) of 0.935, and a root mean square of error (RMSE) of 1.07 × 10−2.

Application of QSPR for prediction of the complexation stabilities of Sm(III) with ionophores applied in lanthanoid sensors

The increase use of ion sensors in different fields leads to many intensive studies to introduce sensing materials or selectophores in the fabrication of ion-selective electrodes. In this study the complexation stability of samarium ion with different ionophores was efficiently predicted by the QSPR model. Since the selectivity of ionophores can be defined by the stability constants of samarium–ionophore complexes, quantitative structure–property relationship (QSPR) studies on complex stability constants (log K) were carried out. The suitable subset of molecular descriptors was calculated and the genetic algorithm was employed to select those descriptors that resulted in the best-fit models. The multiple linear regression (MLR) method was utilized to construct the linear QSPR models. The best model showed most accurate predictions with the Leave-One-Out cross validation (QLOO2 = 0.912), Leave-Group-Out cross validation (QLGO2 = 0.907), external test set and Y-randomization. Also, the applicability domain of the model was analysed by the leverage approach. According to the best of our knowledge, this is the first research on QSPR studies for testing and estimating selectophores in samarium sensors based on complex stability constants. This report could be an experimental guide to find and design of highly selective sensors for Sm(III) ions.

A chemical structure based model for the determination of speed of sound in ionic liquids

In this communication, a reliable quantitative structure–property relationship (QSPR) model is developed to estimate the speed of sound (SS) in ionic liquids at atmospheric pressure. A data set comprising 446 experimental data values for 41 ionic liquids extracted from the NIST Standard Reference Database was used to develop and evaluate the model (80% as the train set and 20% as the test set). In this study, the effects of both anions and cations are considered in the development of the model. Genetic function approximation (GFA) is applied to select the model parameters (molecular descriptors) and develop a 9-variable multivariate linear QSPR model. Statistical analysis of the performance of the model with respect to the data set indicates an average absolute relative deviation (AARD%) of 0.92, a coefficient of determination (R2) of 0.9862, and a root mean square of error (RMSE) of 18.72 m s−1.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

Abstract In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection (SPA) strategy, is used as the descriptor selection and model development method. Then, the support vector machine (SVM) and multiple linear regression (MLR) model are utilized to construct the non-linear and linear quantitative structure–property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.

Predictive Quantitative Structure–Property Relationship Model for the Estimation of Ionic Liquid Viscosity

In this study attention was focused on the development of a predictive model for the viscosity of ionic liquids. A large data set of 435 experimental viscosity data points for 293 ionic liquids incorporating 146 cations and 36 anions was applied for the model derivation. A quantitative structure–property relationship (QSPR) approach was employed to develop a linear model. In this study the effects of both anions and cations were considered in the derivation of the model. Genetic function approximation is applied for the model’s parameter selection (molecular descriptors) and developing a linear QSPR model. Consequently, a simple linear predictive model was obtained with satisfactory results quantified by the following statistical parameters: absolute average deviations (AAD) of the predicted properties from existing experimental values by the GFA linear equation, 8.77%; squared correlation coefficient, 0.8096; and root mean square, 0.232 cP.

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.

Dissipative particle dynamics study on the mesostructures of n-octadecane/water emulsion with alternating styrene–maleic acidcopolymers as emulsifier

For the first time dissipative particle dynamics (DPD) simulations were employed to study the microstructures of an emulsion with alternating copolymers as the emulsifier. To model the alternating copolymers, an angular force was introduced by determining the stiffness parameters based on a linear quantitative structure–property relationship model. We studied the kinetics of emulsion formation by analyzing the time evolution of pressure, temperature, droplet number, the mean end-to-end distance and the morphologies of the emulsified oil droplets. The effect of emulsifier concentration on the mesostructures of the emulsified oil droplets was also discussed and the simulation results can interpret the experimental results on the microscopic level. Accordingly, the DPD method is a powerful tool for investigating emulsions with alternating copolymers and may be extended to drug delivery systems containing these copolymers.

Solubility Parameters of Nonelectrolyte Organic Compounds: Determination Using Quantitative Structure–Property Relationship Strategy

The solubility parameter is considered to be a significant parameter for the chemical industry. In this study, the quantitative structure–property relationship (QSPR) method is applied to develop three models for determination of the solubility parameters of pure nonelectrolyte organic compounds at 298.15 K and atmospheric pressure. To propose comprehensive, reliable, and predictive models, about 1400 data belonging to experimental solubility parameter values of various nonelectrolyte organic compounds are studied. The genetic function approximation (GFA) mathematical approach is applied for selection of proper model parameters (molecular descriptors) and to develop a linear QSPR model. To study the nonlinear relations between the selected molecular descriptors and the solubility parameter, two approaches are pursued: the three-layer feed forward artificial neural networks (3FFANN) and the least square support vector machine (LSSVM). Furthermore, the Levenberg–Marquardt (LM) and genetic algorithm (GA) optimization methods are respectively implemented to optimize the 3FFANN and LSSVM models. Consequently, we obtain three predictive models with satisfactory results quantified by the following statistical parameters: absolute average relative deviation (AARD) of the represented/predicted properties from existing experimental values by the GFA linear equation of 4.6% and squared correlation coefficient of 0.896; AARD of the QSPR-ANN model of 3.4% and squared correlation coefficient of 0.941; and AARD of 3.1% and squared correlation coefficient of 0.947 evaluated by the QSPR-LSSVM model.

Prediction of gas/particle partitioning coefficients of semi volatile organic compounds via QSPR methods: PC-ANN and PLS analysis

Linear and non-linear quantitative structure property relationship (QSPR) models for predicting the gas/particle partitioning coefficients of semivolatile organic compounds were developed based on partial least squares (PLS) and artificial neural network (ANN) to identify a set of structurally based numerical descriptors. Multilinear regression (MLR) was used to build the linear QSPR models using combination of the compounds structural descriptors and topological indices related to environmental conditions such as temperature, pressure and particle size. The prediction results for PLS and ANN models give very good coefficient of determination (0.97). In consistent with experimental studies, it was shown that linear and non-linear regression analyses are useful tools to predict the relationship between the calculated descriptors and gas/particle partitioning coefficient.

Quantitative structure–property relationship study on first reduction and oxidation potentials of donor-substituted phenylquinolinylethynes and phenylisoquinolinylethynes: Quantum chemical investigation

The relationship between the chemical structure, first reduction and oxidation potentials of 30 Phenylquinolinylethyne (PhQE), and Phenylisoquinolinylethyne (PhIE) derivative compounds has been elucidated employing ab initio calculations. Quantum chemical calculations (HF/6-31G) were carried out to obtain: the optimized geometry, energy levels, quantum chemical indices, charges and dipole moments of these compounds. The quantitative structure–property relationship (QSPR) of PhQE and PhIE was studied for the first reduction ( E red), and the first oxidation ( E ox) potentials. 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) was utilized to construct linear QSPR models. The resulting QSPR equations indicated that the orbital energies, quantum chemical indices ( i.e. electronegativity and softness) and localization of charge in molecules are important factors in the first oxidation and reduction potentials of PhQE and PhIE. The quantum-chemical calculations show that the HOMO and LUMO of both PhQE and PhIE derivatives are localized on the donor-substituted phenyl moiety, and the quinolinyl and isoquinolinyl acceptor moiety respectively. Thus, it was proposed that the first reduction and oxidation potentials can be ascribed to reduction at the quinolinyl acceptor moiety, and oxidation at the donor-substituted phenyl moiety.

QSPR Prediction of Aqueous Solubility of Drug-Like Organic Compounds

A quantitative structure property relationship (QSPR) study was performed to develop a model that relates the structures of 150 drug organic compounds to their aqueous solubility (log S(w)). Molecular descriptors derived solely from 3D structure were used to represent molecular structures. A subset of the calculated descriptors selected using stepwise regression that used in the QSPR model development. Multiple linear regression (MLR) is utilized to construct the linear QSPR model. The applied multiple linear regression is based on a variety of theoretical molecular descriptors selected by the stepwise variable subset selection procedure. Stepwise regression was employed to develop a regression equation based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The final regression equation included three parameters that consisted of octanol/water partition coefficient (log P), molecular volume (MV) and hydrogen bond forming ability (HB), of the drug molecules, all of which could be related to solubility property. Application of the developed model to a testing set of 40 drug organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of aqueous solubility for molecules not yet synthesized. The prediction results are in good agreement with the experimental values. The root mean square error of prediction (RMSEP) and square correlation coefficient (R(2)) of prediction of log S(w) were 0.0959 and 0.9954, respectively.