Functional Group-Guided Tokenization for Molecular Representation Learning

As a foundation for molecular property prediction and drug discovery, molecular representation learning has attracted considerable attention. Molecules are naturally represented as graphs composed of atoms and bonds. So, most existing methods employ graph neural networks to learn molecular representations. However, these methods ignore functional groups, which play a critical role in determining molecular properties. As a result, the learned representations may fail to capture the intrinsic information that determines molecular properties, thereby limiting their out-of-distribution generalization. To address this issue, we propose a functional group-guided tokenization method that transforms a molecular into a token graph to capture both functional groups and their interactions. This information is then integrated to obtain the final molecular representation. Extensive experiments on benchmark data sets demonstrate that our method effectively improves the out-of-distribution generalization, showing its ability to learn intrinsic molecular representations that remain invariant across distributions.