GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.

Combination drug therapy is an effective approach to combating drug resistance and enhancing therapeutic efficacy in complex diseases such as cancer. Nevertheless, discovering synergistic drug pairs remains difficult because of the enormous combinatorial possibilities and the context-dependent, nonlinear interactions among drugs and cellular systems. Although recent computational methods have advanced drug synergy prediction, they often fail to preserve pharmacologically meaningful molecular substructures, capture global properties of complex molecular graphs, or model the varying importance of genes across cellular contexts. In this study, we propose GATESynergy, a novel deep learning framework for predicting drug synergy that combines a hierarchical gated gene-aware encoder (HiGate) with a molecular global-local aggregator (MoGLA). Specifically, MoGLA integrates local graph message-passing with global self-attention to jointly model functional motifs and long-range structural dependencies, overcoming the inability of conventional GNNs to simultaneously preserve pharmacologically meaningful substructures and capture global molecular topology. HiGate employs a residual gene-wise gating mechanism that adaptively weights genes according to their contextual relevance, yielding context-specific cell-line representations that address the neglect of gene-level importance in existing encoders. Furthermore, we propose a multi-head interactive additive attention module that uses global query summarization to efficiently fuse drug-drug-cell line representations and capture diverse synergistic interaction patterns. Extensive benchmarking results demonstrate that GATESynergy consistently surpasses current state-of-the-art methods. Moreover, a case study on 42 FDA-approved drugs further validates the effectiveness of GATESynergy in discovering novel synergistic drug combinations. The source data and code are available at https://github.com/coding-in-github/GATESynergy .