In silico package models for deriving values of solute parameters in linear solvation energy relationships

ABSTRACT

Environmental partitioning influences fate, exposure and ecological risks of chemicals. Linear solvation energy relationship (LSER) models may serve as efficient tools for estimating environmental partitioning parameter values that are commonly deficient for many chemicals. Nonetheless, scarcities of empirical solute parameter values of LSER models restricted the application. This study developed and evaluated in silico methods and models to derive the values, in which excess molar refraction, molar volume and logarithm of hexadecane/air partition coefficient were computed from density functional theory; dipolarity/polarizability parameter, solute H-bond acidity and basicity parameters were predicted by quantitative structure–activity relationship models developed with theoretical molecular descriptors. New LSER models on four physicochemical properties relevant with environmental partitioning (n-octanol/water partition coefficients, n-octanol/air partition coefficients, water solubilities, sub-cooled liquid vapour pressures) were constructed using the in silico solute parameter values, which exhibited comparable performance with conventional LSER models using the empirical solute parameter values. The package models for deriving the LSER solute parameter values, with advantages that they are free of instrumental determinations, may lay the foundation for high-throughput estimating environmental partition parameter values of diverse organic chemicals.

KEYWORDS:

• Solute parameter
• environmental partition parameters
• in silico model
• quantitative structure–activity relationship
• linear solvation energy relationship

Acknowledgements

All the authors sincerely acknowledge the National Natural Science Foundation of China and the National Key R&D Program of China for providing funding.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2162576.

Additional information

Funding

This study was supported by the [National Natural Science Foundation of China] under Grant [22136001] and the [National Key R&D Program of China] under Grant [2022YFC3902100].