Dual representation learning-based drug synergy prediction via sequence and molecular network reconstruction
The DRL-DSP is proposed, a novel dual representation learning framework designed to enhance drug synergy prediction by integrating molecular-level features from SMILES sequences with graph-level relational information from reconstructed molecular networks.