Deep Learning for Molecular Property Prediction with Logical Background Knowledge
TL;DR
This work proposes an enhanced learning framework that deeply integrates structured logical knowledge within GNN models, and demonstrates that incorporating domain-specific relational knowledge leads to better generalization and robustness compared to standard GNNs.
Abstract
Graph Neural Networks (GNNs) have emerged as powerful tools for molecular property prediction by leveraging graph-structured data representations of chemical compounds. However, standard GNN architectures are completely generic and do not allow for explicitly encoding domain-specific chemical knowledge, such as functional groups, molecular motifs, and atomic interactions, which can be critical for accurate predictions, especially in low-data regimes. In this work, we propose an enhanced learning framework that deeply integrates structured logical knowledge within GNN models. Particularly, by formulating the underlying message-passing GNN mechanisms as logic-driven transformations, we bridge the gap between symbolic domain knowledge and deep learning, improving predictive accuracy and explainability. Empirical results on multiple benchmark molecular datasets demonstrate that incorporating domain-specific relational knowledge leads to better generalization and robustness compared to standard GNNs.