Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction
Fig 5
Performance comparison on Drug Bank dataset.
The top panel shows accuracy comparison across five methods (CNN-DDI, GMPNN, SSF-DDI, Graph CL, and Graph Former-CL), with Graph Former-CL achieving 98.20% accuracy. Statistical significance tests confirm improvements with p < 0.001 against major baselines. The inductive setting results show Graph Former-CL achieving 82.45% accuracy on novel drugs (+5.23% improvement). The bottom panel displays AUC performance comparison, with Graph Former-CL achieving the highest AUC of 99.34%.