QSPRs for Predicting Equilibrium Partitioning in Solvent–Air Systems from the Chemical Structures of Solutes and Solvents

• Published in: Journal of solution chemistry 2022-09, Vol.51 (9), p.1101-1132
• Main Author: Brown, Trevor N.
• Format: Article
• Language: English
• Subjects: Chemical partition; Chemistry; Chemistry and Materials Science; Condensed Matter Physics; Correlation; Free energy; Geochemistry; Industrial Chemistry/Chemical Engineering; Inorganic Chemistry; Iterative methods; Mathematical models; Oceanography; Organic liquids; Parameter uncertainty; Partitioning; Physical Chemistry; Predictions; Solvation; Solvents
• ISSN: 0095-9782; 1572-8927
• DOI: 10.1007/s10953-022-01162-2

Poly-parameter Linear Free Energy Relationships (PPLFERs) based on the Abraham solvation model are a useful tool for predicting and interpreting equilibrium partitioning of solutes in solvent systems. The focus of this work is neutral organic solutes partitioning in neutral organic liquid solvent-air systems. This is a follow-up to previous work (Brown, 2021) which developed predictive empirical correlations between solute descriptors and system parameters, allowing system parameters to be predicted from the solute descriptors of the solvent. A database of solute descriptors, and a database of system parameters supplemented by empirical predictions, form the basis for the development of new Quantitative Structure Property Relationships (QSPRs). A total of 11 QSPRs have been developed for the E , S , A , B and L solute descriptors, and the s , a , b , v , l , and c system parameters. The QSPRs were developed using a group-contribution method referred to as Iterative Fragment Selection. The method includes robust internal and external model validation and a well-defined Applicability Domain, including estimates of prediction uncertainty. System parameters can also be predicted by combining the solute descriptor QSPRs and the empirical correlations. The predictive power of PPLFERs applied using different combinations of experimental data, empirical correlations, and QSPRs are externally validated by predicting partition ratios between solvents and air. The uncertainty for predicting the log 10 K SA of diverse solutes in diverse solvents using only the new QSPRs and empirical correlations is estimated to be one log 10 unit or less.