The feature importances for the different models highlight the varying roles each descriptor plays in predicting the target variable. We have shown the feature importance for RF and XGBoost in Figure and for Gradient Boosting and LightGBM in Figure in SI. In both the Random Forest and Gradient Boosting models, the polar surface area (psa) emerges as the most important feature, contributing 50%, followed by the pol term, which accounts for approximately 30%. It is to be noted that polar surface area and non-polar surface area are complementary features. In this work, we have taken PSA; however, taking PSA as a feature implicitly includes non-polar surface area also. Hence, the importance of PSA as a feature indicates the importance of polarity of surface areas in general. These polarity of surface area and the pol term dominate the prediction capabilities of these models, suggesting that molecular surface properties play a crucial role in the prediction task. Other descriptors like the number of hydrogen bond donors (n donors), rotatable bonds (nrotb), acceptors (n acceptors), and logP contribute significantly less. This highlights a strong dependence on molecular polarity and surface area in these ensemble tree-based models.