Bond Before Brain: What Actually Drives Molecular MPNNs
TL;DR for operators Molecular GNN selection is often sold as a choice among branded architectures: DMPNN, AttentiveFP, Graphormer, and the rest of the respectable parade. This paper asks a more useful question: before buying the whole architecture, which part of the message-passing pipeline is actually carrying the performance signal? The answer, within this study’s controlled 2D setting, is message construction. The authors benchmark 84 molecular MPNN configurations across ten MoleculeNet tasks by varying three operator families: message-seed initialization, node-edge fusion, and node update. They hold sum aggregation, sum readout, featurization, scaffold splits, tuning protocol, and statistical analysis fixed. That makes the benchmark less glamorous than a new model launch, and substantially more useful. ...