Baseline. Given the similarity of the PLEC implicit graph to the proximity graph data structure, we used PLEC as the primary baseline model for the study. We used the same feed-forward network and hyperparameter optimization scheme (as discussed above) to evaluate PLEC performance for comparability. We also evaluated the models on the CASF 2016 splits to provide a more extensive comparison to published models. Controls for shortcut learning. We used three separate adversarial controls to stress-test each model: frozen MPNN, proximity-edge ablation, and ligand only. We froze encoder weights in the 'frozen MPNN' control before training the model to yield a non-learnable, arbitrary graph representation. The proximity-edge ablation control removed the proximity edges from the graph to test how important message passing between the ligand and protein nodes was to model performance. Finally, the ligand-only control removed all non-ligand atoms and edges.