On the other hand, semi_resampling outperformed no_resampling in four out of five adjusted metrics and provided a more equalized distribution of predicted actives ratio. This confirmed the data augmentation usefulness even if the data balance in the test set differed from that of the training set. This was consistent with the finding that the predicted proportion of positives of the proteochemometrics model was explained by the actual data balance in the test set, rather than that of the training set. We also found that proteins with more interactions were better predicted. While we cannot extrapolate these results to all the proteins and imbalance distributions, this sets a sensible starting point for improving proteochemometrics modelling and remains consistent with the corresponding data imbalance studies on QSAR models. Resampling_before_clustering, where resampling per protein is applied prior to clustering and splitting; resampling_after_clustering, where data is first clustered and splitted and then each protein activity data in each set is resampled; semi_resampling, in which the splitting is performed and then the test set is kept without resampling but the training+validation set is resampled and clustered; and no_resampling, in which the imbalance of the original data is kept and clustering is applied prior to splitting.