Advancing Molecule Invariant Representation via Privileged Substructure Identification

Graph neural networks (GNNs) have revolutionized molecule representation learning by modeling molecules as graphs, with atoms represented as nodes and chemical bonds as edges. Despite their progress, they struggle with out-of-distribution scenarios, such as changes in size or scaffold of molecules with identical properties. Some studies attempt to mitigate this issue through graph invariant learning, which penalizes prediction variance across environments to learn invariant representations. But in the realm of molecules, core functional groups forming privileged substructures dominate molecular properties and remain invariant across distribution shifts. This highlights the need for integrating this prior knowledge and ensuring the environment split compatible with molecule invariant learning. To bridge this gap, we propose a novel framework named MILI. Specifically, we first formalize molecule invariant learning based on privileged substructure identification and introduce substructure invariance constraint. Building on this foundation, we theoretically establish two criteria for environment splits conducive to molecule invariant learning. Inspired by these criteria, we develop a dual-head graph neural network. A shared identifier identifies privileged substructures, while environment and task heads generate predictions based on variant and privileged substructures. Through the interaction of two heads, the environments are split and optimized to meet our criteria. The unified MILI guarantees that molecule invariant learning and environment split achieve mutual enhancement from theoretical analysis and network design. Extensive experiments across eight benchmarks validate the effectiveness of MILI compared to state-of-the-art baselines.