The present work was devoted to explore the quantitative structure-property relationships for gas-to-ionic liquid partition coefficients (log KILA ). A series of linear models were first established for the representative dataset (IL01). The optimal model was a four-parameter equation (1Ed) consisting of two electrostatic potential-based descriptors ( and Vs,max ), one 2D matrix-based descriptor (J_D/Dt) and dipole moment (μ). All of the four descriptors introduced in the model can find the corresponding parameters, directly or indirectly, from Abraham's linear solvation energy relationship (LSER) or its theoretical alternatives, which endows the model good interpretability. Gaussian process was utilized to build the nonlinear model. Systematical validations, including 5-fold cross-validation for the training set, the validation for test set, as well as a more rigorous Monte Carlo cross-validation were performed to verify the reliability of the constructed models. Applicability domain of the model was evaluated, and the Williams plot revealed that the model can be used to predict the log KILA values of structurally diverse solutes. The other 13 datasets were also processed in the same way, and all of the linear models with expressions similar to equation 1Ed were obtained. These models, whether linear of nonlinear, represent satisfactory statistical results, which confirms the universality of the method adopted in this study in QSPR modeling of gas-to-IL partition.
Keywords: QSPR; electrostatic potential; ionic liquid; nonlinear modeling; partition coefficient.
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