Quantum-Inspired Dual-Path Graph Learning for Multimodal Biomedical Triadic Prediction

Biomedical triadic prediction identifies context-dependent associations among molecular entities (e.g., RNAs, genes, and proteins), drugs, and diseases to support drug repurposing and candidate screening. However, molecular sequences, chemical structures, and textual descriptions differ in dimension and statistical properties, making direct fusion difficult. Moreover, missing modality features limit the information available for prediction, while noisy features can interfere with useful signals from other modalities. We propose Quantum-Inspired Dual-Path Graph Learning (QDGL) to integrate heterogeneous modalities and improve triadic prediction with missing or noisy modality features. First, Hilbert-sphere encoding maps modality features onto a shared complex unit sphere through learnable amplitudes and phases, providing a common space for fusion. Second, reliability-weighted fusion learns entity-specific weights to adjust the contributions of available modalities and constructs both vector representations and mixed density states. The vector path aggregates neighborhood features, while the density path propagates mixed states through state matching. Together, these paths combine available modality features and graph relations to mitigate the impact of missing features. Vector and measurement-based tensor interactions are combined for triadic association classification and disease ranking given a molecule–drug pair. Experiments on three biomedical benchmarks (ncRNADrug, CTD, and PrimeKG) show that QDGL achieves the highest mean Hit@1, MRR, and AUC among the compared methods, with maximum relative improvements of 12.04%, 8.05%, and 4.72%, respectively, over the strongest baseline for each dataset and metric. Further experiments demonstrate generalization to unseen entity pairs and robustness to missing features and structural feature perturbations, while ablations support the effectiveness of key designs.