Dual representation learning-based drug synergy prediction via sequence and molecular network reconstruction

Abstract

Predicting drug synergy is crucial for optimizing drug combination therapies. However, it remains a challenging task to extract and integrate complex relational information from multiple data dimensions. This study proposes the DRL-DSP, 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. Our approach employs a SMILES-based data augmentation strategy, where randomized SMILES sequences are generated to enrich sequence representation diversity and enable a more comprehensive exploration of drug characteristics. Additionally, similarities between drug pairs are computed based on SMILES sequences to capture molecular relationships. A network is constructed for graph-level feature aggregation by integrating similarity-based edge weights into the adjacency matrix as an affinity matrix and incorporating feature matrices that reflect molecular properties. This combined representation improves the model's capability to capture molecular and relational information simultaneously. By utilizing encoding-decoding techniques for structural information of SMILES and convolution functions for molecular network representations, DRL-DSP integrates these complementary data resources to enhance drug synergy prediction. Furthermore, experiments under various conditions are conducted to verify the performance of DRL-DSP. Our approach addresses the limitations of single-modality methods and establishes a new paradigm for drug synergy prediction by integrating molecular and relational representations into a more effective and accurate framework.