AINeutralarXiv – CS AI · 14h ago6/10
🧠
What drives performance in molecular MPNNs? An operator-level factorial benchmark
Researchers present a factorial benchmark decomposing 2D molecular message-passing neural networks into 84 distinct configurations to identify which operator components drive molecular property prediction performance. The study finds that message construction methods significantly outweigh update complexity in determining model effectiveness, with concatenation-based mixing showing superior performance in differentiating molecular structures.