To combat this, we used Stochastic Negative Addition. Since protein-ligand binding is rare, if we choose a random molecule and random target from the dataset, we can assume that the compound is inactive against the target. By adding these putative inactives to our dataset, we alleviate the issue of being able to guess target properties by simply looking at the ligand. This increased the size of the dataset to 1.2M datapoints. Our results are consistent with this hypothesis. We showed that a model trained on the resulting dataset is forced to learn information about protein-ligand interactions and can indeed generalize to new targets.