where denotes the concatenation of the individual fingerprints of HBA and HBD molecules, the molar ratios and the temperature and pressure conditions. Utilizing the DES fingerprint f DES ∈ R 334 , we trained a random forest regression model comprising 100 estimators, as implemented by the scikit-learn library . The training process aimed at minimizing the mean squared error, with the decision trees allowed to expand until achieving pure leaves. The adoption of MACCS fingerprints as molecular descriptors, noted for their simplicity, in conjunction with a random forest model, enables a comparative analysis against more computationally demanding molecular representations, such as the previously introduced COSMO-RS sigma profiles. Additionally, the integration of SHapley Additive exPlanations (SHAP) with the random forest model provided a layer of interpretability. This methodological choice permits the identification and visual mapping of the most influential MACCS keys within the DES mixture, thereby elucidating their contribution to the model predictions.