Notably, Feinberg et al. present PotentialNet for various molecular applications, including PDBbind. Unfortunately, the limited information about implementation and the unavailability of their codebase makes direct comparison with PotentialNet challenging. Additionally, a recent report by Cho et al. has further explored this approach, emphasizing a single architecture, only the PDBbind Refined dataset, and a slightly different graph generation procedure than the one used by PotentialNet or herein. Finally, two recent approaches have applied novel attention-based architectures with good effect on this task. In contrast to these previous works, this paper introduces PGN for optimized message-passing schemes. It provides an open-source implementation of MPNN architectures based on Gated-Graph Neural Networks and Directed MPNNs 10 to predict ligand-receptor properties (Figure ). Our experimentation with message-passing parameters using our open-source codebase may be a guide and tool for scientists interested in applying MPNNs to their tasks. The modular nature of our package will also allow for simple testing of different graph generation schemes and new MPNN architectures.