What drives performance in molecular MPNNs? An operator-level factorial benchmark
Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how specific message-passing operators affect performance. We present an operator-level factorial benchmark that decomposes 2D molecular MPNNs into the three families of message-seed initialization, node-edge fusion, and node update operators. The resulting 84 configurations are benchmarked on ten MoleculeNet datasets under a shared experimental setup and statistical analysis protocol. Across this controlled design, performance variation is associated primarily with message construction rather than update complexity. Mes...
| Authors | Panyu Jiao, Shuizhou Chen, Yiheng Shen et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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