Interestingly, the XGBoost model demonstrates a different feature importance distribution, where the number of acceptors (n acceptors) becomes the most dominant feature, contributing 30% to the predictions. Pol term and psa play smaller but still significant roles, contributing around 18-22%. This indicates that the XGBoost model is more sensitive to hydrogen bond acceptor characteristics compared to the other models. LightGBM also highlights psa, logP , and pol term as the most critical features. These results suggest that while molecular surface and polarity remain crucial across models, each model places a different emphasis on these features based on their algorithmic structure.