DeepSTFSynergy: A multi-scale structural information fusion method for personalized drug combination prediction

Efficiently predicting drug synergy is crucial for developing personalized cancer combination therapy regimens. However, existing methods primarily focus on single-scale structural information and fail to explicitly model the interactions between multi-scale structural information from cell lines and drug pairs, limiting their ability to capture the complex molecular mechanisms of synergistic effects. To tackle these challenges, we propose DeepSTFSynergy, a multi-scale structural information fusion framework for personalized drug combination prediction. DeepSTFSynergy introduces three parallel attention-based subnetworks that comprehensively extract the interaction features of drugs at atomic, sub-structural and global structural scales to adaptively capture multi-scale molecular interactions for predicting synergy. Meanwhile, we design a novel cell line-specific cross-modal fusion mechanism that employs gating units to dynamically identify the contributions of three-scale molecular interaction information to synergistic effects in specific cell lines, thereby filtering out non-critical information and efficiently fusing the features of drugs and cell lines. Comprehensive experiments across real-world benchmark datasets reveal that DeepSTFSynergy exhibits superior performance over current leading approaches in both regression and classification tasks. Case studies also illustrate that the novel drug combinations predicted by DeepSTFSynergy align with previous studies. Moreover, visualization analysis reveals the model's capability to identify atomic structures and substructures associated with synergy. By quantifying their relative contributions in combination therapy, it provides an interpretable perspective for understanding synergistic mechanisms and assisting personalized treatment decision-making.