In this section, we performed experiments to evaluate 1) whether MPNNs would offer advantages over fingerprint and other baseline methods, 2) assess the importance of proximity features for protein-ligand models, and 3) whether these models could be effective predictors of experimental binding affinity. To answer these questions, we systematically benchmarked multiple approaches across PDBBind and D4 Dock Datasets and evaluated performance based on RMSE and Pearson Correlation Coefficient (PCC). PCC is more relevant for rank ordering compounds for testing, whereas RMSE is more relevant for predicting raw binding energies in isolation and therefore the inclusion of both gives a better picture of usefulness than either alone.