We chose an oversampling method to balance data since oversampling was shown to improve performance in the Korkmaz study of data imbalance in DL-based QSAR and in a systematic study of data imbalance with CNNs. Oversampling methods increase the number of samples in the minority class to create a balanced data set. Specifically, we used the SMOTE oversampling technique, which creates synthetic data points of the minority class similar to those available. Resampling with SMOTE was done in a per protein basis, so that each protein would be balanced. Some proteins had to be discarded in certain strategies, since there were either only active or inactive ligands, or the number of samples in the minority class was smaller than the number of neighbors used for constructing the synthetic samples (k = 5) and SMOTE was not applicable.