We studied the impact of data balancing strategies on a DL model. We followed the Korkmaz strategy of selecting a simple, well-established architecture whose complexity issues would not be a confounder of the factor under study. We refrained from using Long Short-Term Memory networks since they have convergence issues when training sequences longer than 1000 elements. Model hyperparameters were tuned using the validation set, choosing the simplest working architecture. As in our previous work, 8 the DL PCM model consisted of two analysis blocks. The amino acid sequence analysis block was a 1D convolutional neural network. The fingerprints analysis block consisted of a feed-forward neural network.