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Recently, graph neural networks (GNNs) have been successfully applied topredicting molecular properties, which is one of the most classicalcheminformatics tasks with various applications. Despite their effectiveness,we empirically observe that training a single GNN model for diverse moleculeswith distinct structural patterns limits its prediction performance. In thispaper, motivated by this observation, we propose TopExpert to leveragetopology-specific prediction models (referred to as experts), each of which isresponsible for each molecular group sharing similar topological semantics.That is, each expert learns topology-specific discriminative features whilebeing trained with its corresponding topological group. To tackle the keychallenge of grouping molecules by their topological patterns, we introduce aclustering-based gating module that assigns an input molecule into one of theclusters and further optimizes the gating module with two different types ofself-supervision: topological semantics induced by GNNs and molecularscaffolds, respectively. Extensive experiments demonstrate that TopExpert hasboosted the performance for molecular property prediction and also achievedbetter generalization for new molecules with unseen scaffolds than baselines.The code is available at https://github.com/kimsu55/ToxExpert.

Learning Topology-Specific Experts for Molecular Property Prediction