Expressivity and Generalization: Fragment-Biases for Molecular GNNs
This work addresses the need for more accurate and generalizable molecular modeling for computational chemistry and drug discovery, representing a significant but incremental advance over existing fragment-biased methods.
The authors tackled the problem of improving molecular property prediction by developing a new Graph Neural Network (GNN) architecture with fragment-based inductive biases, resulting in outperforming all GNNs on Peptides and achieving 12% lower error on ZINC and 34% lower error compared to other fragment-biased models.
Although recent advances in higher-order Graph Neural Networks (GNNs) improve the theoretical expressiveness and molecular property predictive performance, they often fall short of the empirical performance of models that explicitly use fragment information as inductive bias. However, for these approaches, there exists no theoretic expressivity study. In this work, we propose the Fragment-WL test, an extension to the well-known Weisfeiler & Leman (WL) test, which enables the theoretic analysis of these fragment-biased GNNs. Building on the insights gained from the Fragment-WL test, we develop a new GNN architecture and a fragmentation with infinite vocabulary that significantly boosts expressiveness. We show the effectiveness of our model on synthetic and real-world data where we outperform all GNNs on Peptides and have 12% lower error than all GNNs on ZINC and 34% lower error than other fragment-biased models. Furthermore, we show that our model exhibits superior generalization capabilities compared to the latest transformer-based architectures, positioning it as a robust solution for a range of molecular modeling tasks.