Abstract
The personalization of cancer treatment through drug combinations is critical for improving healthcare outcomes, increasing effectiveness, and reducing side effects. Computational methods have become increasingly important to prioritize synergistic drug pairs because of the vast search space of possible chemicals. However, existing approaches typically rely solely on global molecular structures, neglecting information exchange between different modality representations and interactions between molecular and fine-grained fragments, leading to limited understanding of drug synergy mechanisms for personalized treatment. To address these limitations, we propose MOSAIC (Multi-granularity crOSs-modAl method for synergIstic drug combinations prediCtion), an AI-driven multi-granularity cross-modal method for personalized synergistic drug combination prediction that considers both molecular and fragment-level features. MOSAIC employs a dual-layer representation system, decomposing molecules into chemically meaningful fragments using the BRICS algorithm, facilitating information exchange between graph and SMILES representations through a bidirectional cross-attention mechanism, and ensuring semantic consistency of different modal representations of the same molecular fragment through a contrastive learning framework. Additionally, we designed a bilinear attention network to capture interactions between fragments of different drugs and dynamically integrate multi-granularity feature relationships through a multi-head attention mechanism. Through extensive experiments on multiple real-world datasets, MOSAIC demonstrates superior performance over state-of-the-art methods. Literature validation confirms its predicted novel drug combinations align with existing clinical evidence, while visualization analyses elucidate its capability to pinpoint key molecular fragments critical for drug synergy, providing valuable insights for personalized treatment planning and remote patient monitoring.