(iii) adding receptor information to the graph. To address (i), we used a frozen MPNN control, where encoder weights were set randomly before training. To investigate (ii), a Proximity Graph was stripped of all proximity edges (i.e., only the ligand and protein covalent bonds associated edges remained in the graph). Finally, to investigate (iii), the proximity graph was stripped of protein and proximity data. All adversarial controls for shortcut learning, aside from the GGNET ligand-only and proximity-edge ablation studies for PDBbind datasets, significantly negatively affected performance, as intended (Figure ). Additionally, we see no clear systematic bias relating error to the number/type of interactions or proximity-graph complexity (SI Figure ).