Unlike Korkmaz, that applied data balancing methods to each training set, we tested four different combinations of balancing, data clustering and splitting (see Figure ): no_resampling, in which bioactivity data for each protein was taken as it was, and clustering was applied in order to perform the splitting; resampling_after_clustering, in which after clustering data and splitting it into training, validation and test, each protein activity data in each set was resampled and attained a 50% actives/inactives proportion; resam-pling_before_clustering, in which, opposite to the previous strategy, resampling was applied prior to clustering and splitting, so while the global protein-wise proportion of actives/inactives was 50%, it did not have to be 50% within each splitting set; and semi_resampling, in which the splitting performed in the no_resampling strategy was reused, the test set was kept without resampling but the training+validation set was resampled, re-clustered and re-splitted into train and validation.