Curiously, training with SNA slightly decreases the performance of both models on the non-SNA test set (AUC 0.71 and 0.78). These observations support our hypothesis that SNA provides a way to force the model to learn information about the ligand-receptor interaction rather than simply exploiting the fact that certain ligand scaffolds are only tested against certain protein classes in the dataset. All models tested outperform the KNN baseline (AUC 0.55).