Quantitative Structure-property Relationship Equation Research Articles

Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation

Development of anionic surfactant compound isvery important because the anionic surfactant class iswidely used in people's lives. For instance,anionic surfactantsare used as food additives and detergents. The novelcompound of sulfonate-basedsurfactantor proposed compound has predictedthe CriticalMicelle Concentration(CMC) value of experiment. Quantitative Structure-Property Relationship (QSPR)analysisbased on semiempiricalZINDO/1 calculationwas conducted to obtain QSPR equation. Theoretical predictorsor independent variable which have an influence on the value of CMC are used to construct QSPR equation. The theoretical predictors areclassified intopredictor of electronic properties, solubility and steric. A total of 108experimentalCMCbelongs to sulfonate-basedsurfactant are calculated their theoretical predictors and analyzed by multiple linear regression. The QSPR equationwhich is obtainedfromthis study contains theimportant theoretical predictors.They are solubility properties, molecular weight, molecular size and net charge of carbon atomin thepolar partof sulfonate-based surfactant. This QSPR equation couldbe used to predict the CMC value of the novelsulfonate-based surfactant.

Quantitative Structure-Property Relationship for Polychlorinated Biphenyls: Toxicity and Structure by Density Functional Theory

Polychlorinated biphenyls (PCBs) with hydrophobicity, lipophilicity and high toxicity, are a group of synthetic persistent organic contaminants, and have caused people widespread concern. In this work, density functional theory (DFT) was employed to calculate some structural parameters of PCBs, such as the negative charge density of the benzene ring (Q), ELUMO, electrophilicity index (ω), and the relationship of toxicity of PCBs with coplanarity, the number of substituted chlorines (NCl), Q, and ω was also investi- gated. Using SPSS17 program, the relevancy of these structural parameters with n-octanol-water partition coefficients (KOW) was analyzed, and multiple linear regression equations of lg KOW for PCBs were constructed. Tests of these quantitative structure-property relationship (QSPR) equations were performed to ensure the stability using cross-validation method, and those equations were also used to predict the toxicity of PCBs. The established QSPR equation based on NCl and ω is simple and reliable, and the predicted lg KOW values of PCBs agree well with experimental results.

QSPR Studies on Impact Sensitivities of High Energy Density Molecules

Impact sensitivity, one of the most important screening factors for novel high energy density materials (HEDMs), was predicted by use of quantitative structure-property relationship (QSPR) based on the electrostatic potential (ESP) values calculated on the van der Waals molecular surface (MSEP). Among various 3D descriptors derived from MSEP, we utilized total and positive variance of MSEP, and devised a new QSPR equation by combining three other parameters. We employed 37 HEDMs bearing a benzene scaffold and nitro substituents, which were also utilized by Rice and Hare. All the molecular structures were optimized at the B3LYP/6-31G(d) level of theory and confirmed as minima by the frequency calculations. Our new QSPR equation provided a good result to predict the impact sensitivities of the molecules in the training set including zwitterionic molecules.

New QSPR equations for prediction of aqueous solubility for military compounds

The development of a new quantitative structure–property relationship (QSPR) model to predict aqueous solubility ( S w) accurately for compounds of military interest is presented. The ability of the new model to predict solubility is assessed and compared to available experimental data. A large set of structurally diverse organic compounds was used in this analysis. SiRMS methodology was employed to develop PLS models based on 135 training compounds and predictive accuracy was tested for 155 compounds selected for that purpose. The use of descriptors calculated only from the 2D level of representation of molecular structure produces a well-fitted and robust QSPR model ( R 2 = 0.90; Q 2 = 0.87). Predictive ability for the model produced in this study on external test set ( R test 2 = 0.81 ) is comparable to the predictive ability of EPI Suite™ 4.0. Consensus solubility predictions using SiRMS and EPI models for 25 compounds of military interest (not included into the training set) have been completed.

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.

Prediction of drug solubility from molecular structure using a drug-like training set

Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable for aliphatic amine groups, number of rotatable bonds and number of aromatic rings) and demonstrated acceptable statistics (r 2 = 0.87, s = 0.51, F = 243.6, n = 191). The model was applied to two test sets consisting of a drug-like set of compounds (r 2 = 0.80, s = 0.68, n = 174) and a set of agrochemicals (r 2 = 0.88, s = 0.65, n = 200). Using the established general solubility equation (GSE) on the training and drug-like test set gave poorer results than the current study. The agrochemical test set was predicted with equal accuracy using the GSE and the QSPR equation. The results of this study suggest that increasing molecular size, rigidity and lipophilicity decrease solubility whereas increasing conformational flexibility and the presence of a non-conjugated amine group increase the solubility of drug-like compounds. Indeed, the proposed structural parameters make physical sense and provide simple guidelines for modifying solubility during lead optimisation.

A QSPR for the plasticization efficiency of polyvinylchloride plasticizers

A simple quantitative structure property relationship (QSPR) for correlating the plasticization efficiency of 25 polyvinylchloride (PVC) plasticizers was obtained using molecular modeling. The plasticizers studied were—aromatic esters (phthalate, terephthalate, benzoate, trimellitate), aliphatic esters (adipate, sebacate, azelate), citrates and a phosphate. The low temperature flex point, T f, of plasticized polyvinylchloride resins was considered as an indicator of plasticization efficiency. Initially, we attempted to predict plasticization efficiency of PVC plasticizers from physical and structural descriptors derived from the plasticizer molecule alone. However, the correlation of these descriptors with T f was not very good with R = 0.78 and r 2 = 0.613. This implied that the selected descriptors were unable to predict all the interactions between PVC and plasticizer. Hence, to account for these interactions, a model containing two polyvinylchloride (PVC) chain segments along with a plasticizer molecule in a simulation box was constructed, using molecular mechanics. A good QSPR equation correlating physical and structural descriptors derived from the model to T f of the plasticized resins was obtained with R = 0.954 and r 2 = 0.909.

Multi‐objective modeling and assessment of partition properties: A GA‐based quantitative structure‐property relationship approach

AbstractIn this work a multi‐objective quantitative structure‐property relationship (QSPR) analysis approach was reported based on the study on three partition properties of 50 aromatic sulfur‐containing carboxylates. Here multi‐objectives (properties) were taken as a vector for QSPR modeling. The quantitative correlations for partition properties were developed using a genetic algorithm‐based variable‐selection approach with quantum chemical descriptors derived from AMl‐based calculations. With the QSPR models, the aqueous solubility, octanol/water partition coefficients and reversed‐phase HPLC capacity factors of sulfur‐containing compounds were estimated and predicted. Using GA‐based multivariate linear regression with cross‐validation procedure, a set of the most promising descriptors was selected from a pool of 28 quantum chemical semi‐empirical descriptors, including steric and electronic types, to integrally build QSPR models. The selected molecular descriptors included the net charges on carboxyl group (Qoc), the 2nd power of net charges on nitrogen atoms (Q2N), the net atomic charge on the sulfur atoms (Qs), the van der Waals volume of molecule (V), the most positive net atomic charge on hydrogen atoms (QH) and the measure of polarity and polarizability (π), which were main factors affecting the distribution processes of the compounds under study. The statistically best QSPR models of six descriptors were simultaneously obtained by GA‐based linear regression analysis. With the selected descriptors and the QSPR equations, mechanisms of partition action of the Sulfur‐containing carboxylates were able to be investigated and interpreted.

Prediction of infinite dilution activity coefficients of chlorinated organic compounds in aqueous solution from quantum-chemical descriptors.

A quantitative structure-property relationship (QSPR) model is developed to correlate the natural logarithm of infinite dilution activity coefficients, ln (gamma(infinity)), of 45 chlorinated organic compounds in aqueous solution from quantum-chemical descriptors. The best correlation equation contains five theoretical molecular descriptors. All descriptors were obtained from the chemical structure of the compounds and have definite physical meaning corresponding to different intermolecular interactions. The model predicts ln (gamma(infinity)) with a correlation coefficients of 0.949 and a standard error of 0.442 ln units. The obtained QSPR equation may be applied to the prediction of gamma(infinity) of other chlorinated organic compounds not present in the data set used for the development of the present model. Copyright 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1851-1856, 2001

Using PM3 Hamiltonian, factor analysis and regression analysis in developing quantitative structure-property relationships for photohydrolysis quantum yields of substituted aromatic halides

Based on the descriptors computed by the PM3 hamiltonian and the application of factor analysis and multiple regression analysis, several Quantitative Structure-Property Relationship (QSPR) equations for disappearance quantum yields of 45 substituted halogen compounds were obtained. After the exclusion of the outliers, the remaining two QSPR equations can be used to predict disappearance quantum yields for compounds with similar structure. By stepwise variable selection regression analysis, two significant factors relating to the disappearance quantum yields of the compounds were identified: the energy of the lowest unoccupied molecular orbital and the electron-nuclear attraction energy of the two-center term for carbon-halogen bonds. By factor analysis, other factors that may relate to the quantum yields of the compounds were obtained. The quantum chemical descriptors used in the study can be determined by calculation from any given structural formula. So the method used in this study permits near a priori prediction of environmental properties for compounds for which the empirical parameters are not readily available.

Correlation of Boiling Points with Molecular Structure. 1. A Training Set of 298 Diverse Organics and a Test Set of 9 Simple Inorganics

A quantitative structure−property relationship (QSPR) treatment of the normal boiling points was performed for a structurally wide variety of organic compounds using the CODESSA (comprehensive descriptors for structural and statistical analysis) technique. A highly significant two-parameter correlation (R2 = 0.9544, s = 16.2 K) employs just two molecular parameters, a bulk cohesiveness descriptor, GI1/3, and the area-weighted surface charge of the hydrogen-bonding donor atom(s) in the molecule. A more refined QSPR model (with R2 = 0.9732 and s = 12.4 K) includes, in addition, the most negative atomic partial charge and the number of the chlorine atoms in the molecule. The four-parameter equation offers an average predicted error of 2.3% for a standard set of compounds with an average experimental error of 2.1%. The QSPR equations developed allow remarkably accurate predictions of the normal boiling points for a number of simple inorganic compounds, including water.