The Importance of Stochastic Negatives As can be seen in Figure , when Banana is trained without SNA, the ligand-only model outperforms the ligand-and-receptor model (AUC 0.80 versus 0.75). The fact that receptor information reduces performance implies that this performance is entirely due to biased protein-ligand co-occurrence within the dataset. Indeed, when we apply SNA to the test set and try the non-SNA models, we see that both versions perform poorly (AUC 0.63 versus 0.59). Thus, these models are unsuitable for prospective virtual screening tasks.