To evaluate the predictability of the ML models in this study, we randomly split the solubility data into a training set (90%) and an internal test set (10%). Model performance was assessed using the coefficient of determination (R²), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The hyperparameters for each model were thoroughly optimized within specified ranges, and once optimal values were identified, the final models were trained on the full training dataset and evaluated using the test set. The optimized hyperparameters for each dataset are summarized in Table . The configuration involving two hidden layers is required exclusively for the WS496 dataset, and the corresponding set of parameters is detailed in Table . MACCS keys, However, are based on fundamental chemical descriptors. Combining two fingerprint types generally enhances prediction accuracy compared to using a single fingerprint, with ECFP maintaining a slight advantage. Ultimately, using all three fingerprint types together results in the highest performance scores, with no significant differences depending on the order of fingerprints. These findings underscore the importance of fixed calculations and features, particularly in MACCS and Avalon fingerprints compared to ECFP, for capturing the atom's connectivity and chemical features.