Training deep learning models on protein-ligand interactions with known crystal structures makes sense in the context of augmenting docking techniques that generate possible ligand poses. However, such explicit knowledge about ligand pose in the binding pocket may be unnecessary for predicting ligand binding scores. Indeed, several methods have been proposed that predict activity given just the ligand chemical graph representation and the 3D receptor structure or the ligand graph and the receptor amino acid sequence. Additionally, it is possible to train models that rely solely on docked poses, as is done by Liu et al. .