ShellGatedGNN: Long-Range Message Passing for 2D Molecular Electronic Property Prediction

Message passing neural networks (MPNNs) provide a general framework for molecular graph modeling. To capture long-range structural information in a molecule, the standard approach is to stack many layers of 1-hop message passing; however, such propagation does not capture complex topology precisely and is prone to oversmoothing as it gets deeper. We propose SGGNN (ShellGatedGNN), a long-range message-passing architecture. It extends classical gated message passing from a single edge to the triple (sender, sender's environment, receiver), where the sender's environment is the structure around the sender. We divide the nodes into shells by their distance to the receiver, a common grouping strategy, so that the nodes in one shell are at the same distance. This shelling is permutation-equivariant, so SGGNN can use a concatenation instead of a sum in the node update, which keeps more information. On QM9 and QM40, SGGNN attains the best performance among the strong baselines on all six electronic properties. On the HOMO-LUMO gap regression task of the large-scale PCQM4Mv2 benchmark, it outperforms the message-passing baselines. Our code is available at the anonymous repository https://anonymous.4open.science/r/SGGNN-114514A.