Compounds whose labels could not be unequivocally assigned were removed from the dataset (e.g., activity < 100 μM, activity > 1 μM, or duplicated compounds with contradictory labels). This resulted in a dataset containing 225 compounds, with 141 of them labeled as active and the remaining 84 labeled as inactive. This dataset was randomly divided in a stratified manner into two subsets: 80% was used to train different binary classification models and select the best ones using leave-one-out cross-validation (LOO-CV), and the 20% remaining was used as an external test set.