Tail—aware heterogeneous graph neural networks for multi—class drug—drug interaction prediction
TL;DR
Among class imbalance strategies tested, Tail-Aware Focal Loss outperforms standard cross-entropy by 3.1% on Macro F1 and 7.4% on Recall, striking a better balance between class performance than either weighted cross-entropy or weighted random sampling.
Abstract
Abstract Motivation Polypharmacy is increasingly common in clinical practice, and the sheer number of possible drug combinations makes manual interaction screening impractical. Early computational approaches relied on chemical similarity metrics and rule-based systems, while subsequent machine learning and deep learning methods improved predictive power but continued to treat drugs as isolated entities, missing the broader biological context that governs interaction behaviour. Graph Neural Network (GNN) based methods address this by modeling drugs alongside proteins, diseases, and side effects in a shared relational graph, but tend to fall short on sparsely represented long-tail interaction classes due to the severe class imbalance that characterizes real-world biomedical interaction data. Results We construct a large-scale heterogeneous biomedical knowledge graph—26 408 nodes across five entity types and 1 679 387 edges across six relation types—and benchmark MLP, GCN, HGT, and RGCN for 105- class DDI prediction. RGCN achieves the strongest overall performance (Macro F1: 0.694, Recall: 0.720), with relation-specific weight matrices proving the critical factor in heterogeneous DDI modelling. Among class imbalance strategies tested, Tail-Aware Focal Loss outperforms standard cross-entropy by 3.1% on Macro F1 and 7.4% on Recall, striking a better balance between class performance than either weighted cross-entropy or weighted random sampling.