We evaluate the performance of the LGBM and RF models on AqSolDB and BigSolDB datasets. The performance of the models is evaluated using three metrics: R 2 , RMSE, and MAE to determine which model architecture and features (Table ) are superior for aqueous solubility (S) and organic solubility (x, mole fraction of organic in solvent) predictions. Prior to training the models, both datasets underwent pre-processing, which is detailed in the methods section of the supporting information. Parity plots (Fig. ) demonstrate the performance of the LGBM models on the aqueous and organic solubility datasets. The values of the three performance metrics obtained for the test set are displayed on the figures. The same kind of parity plots are also provided for the RF model in the supporting information (Fig. ). The RF models had slightly worse score metrics than the LGBM models on the test set and in the nested five-fold CV for both datasets.