These studies got the best performances by controlling data balance by means of undersampling techniques (in the case of Zakharov) and oversampling techniques (in the case of Korkmaz). We chose SMOTE for data balancing, an oversampling technique, since the settings of the Korkmaz study were more aligned with ours and because DL models require a large quantity of training data. Specifically, in four out of five metrics, proteins with more interactions were better predicted (table S17 of the Supporting Information) which was also found in the Korkmaz paper.