MolADR: Multi-Scale Complementary Learning of Dual-Granularity Molecules and Knowledge Graphs for ADR Prediction in Drug Combinations.
TL;DR
MolADR is proposed, a multiscale complementary learning framework that integrates GNN-based KG learning with dual-granularity molecular cross-attention modeling to combine macro-level biomedical associations with microlevel molecular interaction cues, suggesting its ability to highlight chemically plausible atoms and functional groups for organ-level ADR prediction.
Abstract
With the increasing use of multiple medications in clinical practice, accurate and interpretable prediction of organ-level adverse drug reactions (ADRs) induced by drug combinations is essential for drug safety assessment and precision medicine. Existing knowledge graph (KG)-based methods primarily model biomedical associations but leave direct structure-level interactions within drug pairs undercharacterized, while molecular representation methods often rely on whole-molecule or latent substructure encodings, offering limited chemically meaningful evidence for ADR risks. This study proposes MolADR, a multiscale complementary learning framework that integrates GNN-based KG learning with dual-granularity molecular cross-attention modeling to combine macro-level biomedical associations with microlevel molecular interaction cues. Under an emerging-drug setting, MolADR achieves PR-AUC scores of 81.17 ± 3.97, 84.04 ± 4.67, and 74.37 ± 9.90 on three data sets, consistently outperforming state-of-the-art baselines, with further analyses supporting its robustness and suggesting its ability to highlight chemically plausible atoms and functional groups for organ-level ADR prediction.