We were next interested in seeing if this performance carried over to the more complex task of experimental binding prediction using a dataset of 589 docked structures with empirical binding data (see supporting methods). The first approach was to evaluate the experimental ligand with the trained PDBbind refined model; however, we saw no ability for the model to predict experimental binding affinity (Figure ). Interestingly, we saw that finetuning from the D4 dataset to PDBbind dataset also was not helpful, suggesting that the molecular representations learned for each task are likely quite distinct (Figure ). Additionally, when we evaluated the experimental set using our optimized model from the D4 docking score prediction task we saw no discrimination between binders and non-binders; this is not surprising given the binders and non-binders had generally similar docking score distributions. Finally, we tried to use a metric learning approach to distinguish binders from non-binders. Although we saw improvements compared to PLEC (Figure ), proximity edges did not appear to improve performance compared to the ligand autonomous graph networks.