Additionally, it is inappropriate to use DUD-E as a training set due to subtle biases that models can exploit. We then developed a simple graph neural network, Banana (BAsic NeurAl Network for binding Affinity), to directly predict activity from the pocket and ligand graphs. In order to address overfitting in our initial regression model, we used Stochastic Negative Addition (SNA) 33 to augment the dataset with putative inactives. We then trained a classification model on the augmented dataset, which achieved an AUC of 0.72 on the BigBind test set.