Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction
Fig 2
Overview of Graph Former-CL architecture.
The framework processes Drug A (molecular graph) and Drug B (SMILES sequence) through parallel Graph Transformer and Contrastive Learning pathways. The Graph Transformer incorporates spatial encoding, multi-head attention, hierarchical pooling, and feature extraction. The Contrastive Learning module applies domain-specific augmentations including atom masking, bond perturbation, scaffold hopping, and subgraph sampling. Cross-Modal Fusion with cross-attention and adaptive fusion mechanisms integrates the representations, followed by DDI Prediction through drug pair encoding and MLP classifier to output interaction probability scores.