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This paper introduces ResGIN-Att, a novel graph neural network for predicting drug synergy by integrating molecular structure and cell-line genomic profiles. The model uses a residual graph isomorphism network to extract multi-scale topological features of drug molecules, mitigating over-smoothing, and employs a cross-attention module to model drug-drug interactions and identify key substructures. Experiments on five benchmark datasets show ResGIN-Att achieves competitive performance and improved generalization compared to existing methods.
ResGIN-Att's cross-attention mechanism not only boosts drug synergy prediction but also offers a peek into the "why" behind drug interactions by highlighting crucial chemical substructures.
In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast, combination drug therapies can significantly improve therapeutic outcomes through synergistic effects. However, experimentally validating all possible drug combinations is prohibitively expensive, underscoring the critical need for efficient computational prediction methods. Although existing approaches based on deep learning and graph neural networks (GNNs) have made considerable progress, challenges remain in reducing structural bias, improving generalization capability, and enhancing model interpretability. To address these limitations, this paper proposes a collaborative prediction graph neural network that integrates molecular structural features and cell-line genomic profiles with drug-drug interactions to enhance the prediction of synergistic effects. We introduce a novel model named the Residual Graph Isomorphism Network integrated with an Attention mechanism (ResGIN-Att). The model first extracts multi scale topological features of drug molecules using a residual graph isomorphism network, where residual connections help mitigate over-smoothing in deep layers. Subsequently, an adaptive Long Short-Term Memory (LSTM) module fuses structural information from local to global scales. Finally, a cross-attention module is designed to explicitly model drug-drug interactions and identify key chemical substructures. Extensive experiments on five public benchmark datasets demonstrate that ResGIN-Att achieves competitive performance, comparing favorably against key baseline methods while exhibiting promising generalization capability and robustness.
