QSPR model of Henry's law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach

A QSPR Model for Henry's Law Constants of Organic Compounds in Water and Ethanol for Distilled Spirits

Henry's law describes the vapor-liquid equilibrium for dilute gases dissolved in a liquid solvent phase. Descriptions of vapor-liquid equilibrium allow the design of improved separations in the food and beverage industry. The consumer experience of taste and odor are greatly affected by the liquid and vapor phase behavior of organic compounds. This study presents a machine learning (ML) based model that allows quick, accurate predictions of Henry's law constants (kH) for many common organic compounds. Users input only a Simplified Molecular-Input Line-Entry System (SMILES) string or a common English name, and the model returns Henry's law estimates for compounds in water and ethanol. Training was performed on 5,690 compounds. Training data were gathered from an existing database and were supplemented with quantum mechanical (QM) calculations. An extra trees regression model was generated that predicts kH with a mean absolute error of 1.3 in log space and an R2 of 0.98. The model is applied to common flavor and odor compounds in bourbon whiskey as a test case for food and beverage applications.

Henry's Constants of Persistent Organic Pollutants by a Group-Contribution Method Based on Scaled-Particle Theory

A group-contribution method based on scaled-particle theory was developed to predict Henry's constants for six families of persistent organic pollutants: polychlorinated benzenes, polychlorinated biphenyls, polychlorinated dibenzodioxins, polychlorinated dibenzofurans, polychlorinated naphthalenes, and polybrominated diphenyl ethers. The group-contribution model uses limited experimental data to obtain group-interaction parameters for an easy-to-use method to predict Henry's constants for systems where reliable experimental data are scarce. By using group-interaction parameters obtained from data reduction, scaled-particle theory gives the partial molar Gibbs energy of dissolution, Δg̅₂, allowing calculation of Henry's constant, H₂, for more than 700 organic pollutants. The average deviation between predicted values of log H₂ and experiment is 4%. Application of an approximate van't Hoff equation gives the temperature dependence of Henry's constants for polychlorinated biphenyls, polychlorinated naphthalenes, and polybrominated diphenyl ethers in the environmentally relevant range 0-40 °C.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

Prediction of blood:air and fat:air partition coefficients of volatile organic compounds for the interpretation of data in breath gas analysis

In this article, a database of blood:air and fat:air partition coefficients (λ b:a and λ f:a) is reported for estimating 1678 volatile organic compounds recently reported to appear in the volatilome of the healthy human. For this purpose, a quantitative structure-property relationship (QSPR) approach was applied and a novel method for Henry's law constants prediction developed. A random forest model based on Molecular Operating Environment 2D (MOE2D) descriptors based on 2619 literature-reported Henry's constant values was built. The calculated Henry's law constants correlate very well (R(2) test  =  0.967) with the available experimental data. Blood:air and fat:air partition coefficients were calculated according to the method proposed by Poulin and Krishnan using the estimated Henry's constant values. The obtained values correlate reasonably well with the experimentally determined ones for a test set of 90 VOCs (R(2)  =  0.95). The provided data aim to fill in the literature data gap and further assist the interpretation of results in studies of the human volatilome.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

Prediction of Henry's Law Constants via group-specific quantitative structure property relationships

Henry's Law Constants (HLCs) for several hundred organic compounds in water at 25 °C were predicted by Quantitative Structure Property Relationship (QSPR) models, with the division of organic compounds into specific classes to yield more accurate models than generalised ones. Both multiple linear regression (MLR) and artificial neural network (ANN) versions of models were produced for three general cases, encompassing the entire data set; one used the six best descriptors, as determined by maximising the correlation coefficient; another used the twelve best descriptors in a similar manner, whilst the third used the same twelve descriptors as English and Carroll (2001). These achieved, respectively, root-mean square errors (RMSEs) of 0.719, 0.52 and 0.607 log(Hcc) units for the MLR version and 0.601, 0.394 and 0.431 for the test set of the ANN models, where Hcc is the ratio of the compound's concentration in the vapour phase to that in the liquid phase. These were compared with models for six specific chemical classes: (i) alkanes, (ii) cyclic alkanes, (iii) alkenes, (iv) halogenated compounds, (v) aldehydes, ketones and esters grouped together, and (vi) monoaromatics. These group-specific models had RMSEs of 0.153, 0.141. 0.097, 0.168, 0.122 and 0.104 respectively for the MLR versions and 0.684, 0.719, 0.856, 0.784, 0.875 and 0.861 for the test set of the ANN models. It was found that the class-specific models achieved lower RMSEs than the general models, when using MLR models. The use of ANN was found to improve the predictive accuracy of the general models but failed to improve that for the class-specific models vis-à-vis MLR.

Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation

With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silico prediction of such data. One of the chief predictive approaches is QSAR (quantitative structure-activity relationships), which is widely used in many fields. A QSAR approach that is increasingly being used is that of artificial neural networks (ANNs), and this chapter discusses its application to the range of physicochemical properties and toxicities required by REACH. ANNs generally outperform the main QSAR approach of multiple linear regression (MLR), although other approaches such as support vector machines sometimes outperform ANNs. Most ANN QSARs reported to date comply with only two of the five OECD Guidelines for the Validation of (Q)SARs.

QSPR prediction of physico-chemical properties for REACH

For registration of a chemical, European Union REACH legislation requires information on the relevant physico-chemical properties of the chemical. Predicted property values can be used when the predictions can be shown to be valid and adequate. The relevant physico-chemical properties that are amenable to prediction are: melting/freezing point, boiling point, relative density, vapour pressure, surface tension, water solubility, n-octanol-water partition coefficient, flash point, flammability, explosive properties, self-ignition temperature, adsorption/desorption, dissociation constant, viscosity, and air-water partition coefficient (Henry's law constant). Published quantitative structure-property relationship (QSPR) methods for all of these properties are discussed, together with relevant property prediction software, as an aid for those wishing to use predicted property values in submissions to the European Chemicals Agency (ECHA).

QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method

The possible molecular geometries of 134 halogenated methyl-phenyl ethers were optimized at B3LYP/6-31G(*) level with Gaussian 98 program. The calculated structural parameters were taken as theoretical descriptors to establish two new novel QSPR models for predicting aqueous solubility (-lgS(w,l)) and n-octanol/water partition coefficient (lgK(ow)) of halogenated methyl-phenyl ethers. The two models achieved in this work both contain three variables: energy of the lowest unoccupied molecular orbital (E(LUMO)), most positive atomic partial charge in molecule (q(+)), and quadrupole moment (Q(yy) or Q(zz)), of which R values are 0.992 and 0.970 respectively, their standard errors of estimate in modeling (SD) are 0.132 and 0.178, respectively. The results of leave-one-out (LOO) cross-validation for training set and validation with external test sets both show that the models obtained exhibited optimum stability and good predictive power. We suggests that two QSPR models derived here can be used to predict S(w,l) and K(ow) accurately for non-tested halogenated methyl-phenyl ethers congeners.

Prediction of physicochemical properties

Physicochemical properties are key factors in controlling the interactions of xenobiotics with living organisms. Computational approaches to toxicity prediction therefore generally rely to a very large extent on the physicochemical properties of the query compounds. Consequently it is important that reliable in silico methods are available for the rapid calculation of physicochemical properties. The key properties are partition coefficient, aqueous solubility, and pKa and, to a lesser extent, melting point, boiling point, vapor pressure, and Henry's law constant (air-water partition coefficient). The calculation of each of these properties from quantitative structure-property relationships (QSPRs) and from available software is discussed in detail, and recommendations made. Finally, detailed consideration is given of guidelines for the development of QSPRs and QSARs.

PTR-MS measurements and analysis of models for the calculation of Henry's law constants of monosulfides and disulfides

Sulfides are known for their strong odor impact even at very low concentrations. Here, we report Henry's law constants (HLCs) measured at the nanomolar concentration range in water for monosulfides (dimethylsulfide, ethylmethylsulfide, diethylsulfide, allylmethylsulfide) and disulfides (dimethyldisulfide, diethylsulfide, dipropylsulfide) using a dynamic stripping technique coupled to Proton Transfer Reaction-Mass Spectrometry (PTR-MS). The experimental data were compared with literature values and to vapor/solubility calculations and their consistency was confirmed employing the extra-thermodynamic enthalpy-entropy compensation effect. Our experimental data are compatible with reported literature values, and they are typically lower than averaged experimental literature values by about 10%. Critical comparison with other freely available models (modeled vapor/solubility; group and bond additivity methods; Linear Solvation Energy Relationship; SPARC) was performed to validate their applicability to monosulfides and disulfides. Evaluation of theoretical models reveals a large deviation from our measured values by up to four times (in units of Matm(-1)). Two group contribution models were adjusted in view of the new data, and HLCs for a list of sulfur compounds were calculated. Based on our findings we recommend the evaluation and adaption of theoretical models for monosulfides and disulfides to lower values of solubility and higher values of fugacity.

QSPR prediction of flash point of esters by means of GFA and ANFIS

A quantitative structure property relationship (QSPR) study was performed to develop a model for prediction of flash point of esters based on a diverse set of 95 components. The most five important descriptors were selected from a set of 1124 descriptors to build the QSPR model by means of a genetic function approximation (GFA). For considering the nonlinear behavior of these molecular descriptors, adaptive neuro-fuzzy inference system (ANFIS) method was used. The ANFIS and GFA squared correlation coefficient for testing set was 0.969 and 0.965, respectively. The results obtained showed the ability of developed GFA and ANFIS for prediction of flash point of esters.

Air-liquid partition coefficient for a diverse set of organic compounds: Henry's Law Constant in water and hexadecane

The SPARC vapor pressure and activity coefficient models were coupled to successfully estimate Henry's Law Constant (HLC) in water and in hexadecane for a wide range of organic compounds without modification to, or additional parametrization of, either SPARC model. The vapor pressure model quantifies the solute-solute intermolecular interactions in the pure liquid phase, whereas the activity coefficient model quantifies the solute-solvent and solvent-solvent (in addition to the solute-solute) interactions upon placing solute, i, in solvent, j. These intermolecular interactions are factored into dispersion, induction, dipole-dipole, and H-bonding components upon moving a solute molecule from the gas to the liquid phase. The SPARC HLC calculator so produced was tested and validated on the largest experimental HLC data set to date: 1356 organic solutes, spanning a wide range of functional groups, dipolarities and H-bonding capabilities, such as PAHs, PCBs,VOCs, amides, pesticides, and pharmaceuticals. The rms deviation errors for the calculated versus experimental log HLCs for 1222 compounds in water and 563 in hexadecane were 0.456 and 0.192 log [(mol/L)/(mol/L)] units, respectively, spanning a range of more than 13 and 20 log HLC dimensionless units for the compounds in water and hexadecane, respectively. The SPARC calculator web version is available for public use, free of charge, and can be accessed at http://sparc.chem.uga.edu.