Empirical regressions between system parameters and solute descriptors of polyparameter linear free energy relationships (PPLFERs) for predicting solvent-air partitioning

Brown, Trevor N.

doi:10.1016/j.fluid.2021.113035

Cited by 15 publications

(16 citation statements)

References 67 publications

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“…The linear solvation energy relationship (LSER) is a polyparameter linear free energy relationship developed by Abraham and is a multiple linear regression model that describes logarithmic partition coefficients using five solute descriptors. , The iterative fragment selection quantitative structure–activity relationship (IFSQSAR) predicts these solute descriptors. Combined with the published LSER equation for log K aw , IFSQSAR predicts log K aw values based only on the molecular structure. An online toolbox, EAS-E Suite (Ver.…”

Section: Methodsmentioning

confidence: 99%

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Experimental Determination of Air/Water Partition Coefficients for 21 Per- and Polyfluoroalkyl Substances Reveals Variable Performance of Property Prediction Models

Endo

Hammer

Matsuzawa

2023

Environ. Sci. Technol.

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Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals of high environmental concern. However, reliable data for the air/water partition coefficients (K aw), which are required for fate, exposure, and risk analysis, are available for only a few PFAS. In this study, K aw values at 25 °C were determined for 21 neutral PFAS by using the hexadecane/air/water thermodynamic cycle. Hexadecane/water partition coefficients (K Hxd/w) were measured with batch partition, shared-headspace, and/or modified variable phase ratio headspace methods and were divided by hexadecane/air partition coefficients (K Hxd/air) to obtain K aw values over 7 orders of magnitude (10–4.9 to 102.3). Comparison to predicted K aw values by four models showed that the quantum chemically based COSMOtherm model stood out for accuracy with a root-mean-squared error (RMSE) of 0.42 log units, as compared to HenryWin, OPERA, and the linear solvation energy relationship with predicted descriptors (RMSE, 1.28–2.23). The results indicate the advantage of a theoretical model over empirical models for a data-poor class like PFAS and the importance of experimentally filling data gaps in the chemical domain of environmental interest. K aw values for 222 neutral (or neutral species of) PFAS were predicted using COSMOtherm as current best estimates for practical and regulatory use.

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Section: Methodsmentioning

confidence: 99%

“…31,32 The iterative fragment selection quantitative structure−activity relationship (IFSQSAR) 33 predicts these solute descriptors. Combined with the published LSER equation for log K aw , 34 indicator, EAS-E Suite provides an "uncertainty level (UL)", which indicates reliability of the predicted solute descriptors.…”

Section: Shared-headspace Methodmentioning

confidence: 99%

Experimental Determination of Air/Water Partition Coefficients for 21 Per- and Polyfluoroalkyl Substances Reveals Variable Performance of Property Prediction Models

Endo

Hammer

Matsuzawa

2023

Environ. Sci. Technol.

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“…In general, there are two major prediction approaches: Quantitative structure–property relationships (QSPRs) correlating a chemical property of interest with variables describing chemical structure or molecule-level interactions. Examples of chemical structure variables include the “fragments and atoms” used by the Estimation Programs Interface (EPI) Suite, “fragments” used by the Iterative Fragment Selection-QSAR (IFS-QSAR), − and the constitutional, topological, and geometrical “molecular descriptors” used by the OPEn structure–activity/property Relationship App (OPERA) and QSAR-INSubria (QSARINS). , Examples of molecule-level interactions include Abraham solute descriptors reflecting van der Waals interactions and H-bond interactions, used frequently in poly parameter linear free energy relationships (pp-LFERs). , Semiempirical relationships correlating a chemical property of interest solely with other properties, without considering chemical structure or molecule-level interactions. A prominent example is the single-parameter linear free energy relationship (sp-LFER) such as the various forms of the Karickhoff equation correlating the organic carbon-normalized sorption coefficient ( K OC ) with K OW . − Here, we refer to these relationships as “semiempirical” rather than “empirical” because the development of these relationships involves certain mechanistic considerations (e.g., the analogy between soil amorphous organic matter and octanol in the sorption of neutral chemicals), rather than being entirely data-driven. …”

Section: Introductionmentioning

confidence: 99%

“…Quantitative structure–property relationships (QSPRs) correlating a chemical property of interest with variables describing chemical structure or molecule-level interactions. Examples of chemical structure variables include the “fragments and atoms” used by the Estimation Programs Interface (EPI) Suite, “fragments” used by the Iterative Fragment Selection-QSAR (IFS-QSAR), − and the constitutional, topological, and geometrical “molecular descriptors” used by the OPEn structure–activity/property Relationship App (OPERA) and QSAR-INSubria (QSARINS). , Examples of molecule-level interactions include Abraham solute descriptors reflecting van der Waals interactions and H-bond interactions, used frequently in poly parameter linear free energy relationships (pp-LFERs). , …”

Section: Introductionmentioning

confidence: 99%

Chemical Space Covered by Applicability Domains of Quantitative Structure–Property Relationships and Semiempirical Relationships in Chemical Assessments

Zhang,

Sangion,

Wang

et al. 2024

Environ. Sci. Technol.

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A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure−property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol−air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.

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“…[57] used the slightly smaller Abraham Absolv solute parameter database found on the UFZ-LSER internet website [58] in developing their group contribution and deep machine learning methods for estimating solute descriptors. Methods aimed at predicting the Abraham model process coefficients have had mixed success; in part, because experimental-based correlations have been developed for relatively few organic solvents and biphasic partition systems [59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

Sinha

Yang

et al. 2022

Liquids

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Abraham model L solute descriptors have been determined for 149 additional C11 to C42 monomethylated and polymethylated alkanes based on published Kovat’s retention indices based upon gas–liquid chromatographic measurements. The calculated solute descriptors, in combination with previously published Abraham model correlations, can be used to predict a number of very important chemical and thermodynamic properties including partition coefficients, molar solubility ratios, gas–liquid chromatographic and HPLC retention data, infinite dilution activity coefficients, molar enthalpies of solvation, standard molar vaporization and sublimation at 298 K, vapor pressures, and limiting diffusion coefficients. The predictive computations are illustrated by estimating both the standard molar enthalpies of sublimation and the enthalpies of solvation in benzene for the monomethylated and polymethylated alkanes considered in the current study.

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Empirical regressions between system parameters and solute descriptors of polyparameter linear free energy relationships (PPLFERs) for predicting solvent-air partitioning

Cited by 15 publications

References 67 publications

Experimental Determination of Air/Water Partition Coefficients for 21 Per- and Polyfluoroalkyl Substances Reveals Variable Performance of Property Prediction Models

Experimental Determination of Air/Water Partition Coefficients for 21 Per- and Polyfluoroalkyl Substances Reveals Variable Performance of Property Prediction Models

Chemical Space Covered by Applicability Domains of Quantitative Structure–Property Relationships and Semiempirical Relationships in Chemical Assessments

Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

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