Machine learning model feature importance analysis was conducted with the feature scores of the XGBoost model based on the total gain. We employed the label-spreading semi-supervised learning approach implemented in scikit-learn to identify non-hemilabile ligands. Here, the full negative class from the small set combined with only 80% of positively labeled hemilabile ligands were used for label spreading. These hemilabile ligands are the same ligands as the ones used in the training set of random and semi-supervised sets. Ligands that were assigned to the negative class with high confidence (>0.995) based on label-spreading, were assigned a negative label. The pseudo-label set was then randomly sampled to supplement the original dataset to obtain an equal number of positive and negative examples for further examination.