Combination therapy presents a transformative approach to treating complex diseases such as cancer by mitigating toxicity and resistance challenges inherent to monotherapy. A critical gap in current computational methods, however, lies in their inability to model cell-specific drug responses and dynamic drug-cell interactions, which are key factors in accurately predicting synergistic drug pairs. To address this, we propose DGSS, a novel Dynamic Interaction Graph Neural Network with Cell-Specific Drug Substructure Awareness, designed to explicitly capture two pivotal aspects: (1) drug substructures that drive efficacy in specific cellular environments, and (2) dynamic, context-dependent interactions between drugs and cell lines. Our framework introduces two technical innovations: a hierarchical attention mechanism that identifies cell-line-specific drug substructures by correlating molecular subgraphs with genomic features, and a dynamic graph network that models evolving cell-line states during drug exposure. Extensive experiments under three data partitioning strategies across 12 datasets demonstrate DGSS's robustness, consistently outperforming all state-of-the-art baseline models. On the Loewe Synergy dataset, the model achieved AUROC and AUPRC of 96.0% and 85.5%, respectively, and exhibited good stability. By bridging molecular substructure dynamics with cellular context, DGSS advances precision in synergy prediction, offering a data-driven framework to optimize combination therapies in personalized oncology.
Ge et al. (Tue,) studied this question.
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