基于计算模型的协同药物组合预测任务已在癌症领域得到广泛研究和应用。然而，大多数模型只考虑药物对和特定细胞系之间的相互作用，而没有考虑药物-药物和细胞系-细胞系的多重生物学关系，这些生物学关系也在很大程度上影响协同机制。为此，我们在这里提出了一种多模态深度学习框架，称为MDNNSyn，它充分应用多源信息并训练多模态特征来推断潜在的协同药物组合。 MDNNSyn通过在药物协同超图上实现多层超图神经网络来提取拓扑模态特征，并通过相似性策略构造语义模态特征。然后采用具有门控神经网络的多模态融合网络层进行协同分数预测。 MDNNSyn 与 DrugCombDB 和 Oncology-Screen 数据集上的五种经典且最先进的预测方法进行了比较。该模型在两个数据集上的曲线下面积 (AUC) 得分分别为 0.8682 和 0.9013，比第二好的模型提高了 3.70% 和 2.71%。案例研究表明 MNNNSyn 能够检测潜在的协同药物组合。

Synergistic drug combination prediction tasks based on the computational models have been widely studied and applied in the cancer field. However, most of models only consider the interactions between drug pairs and specific cell lines, without taking into account the multiple biological relationships of drug-drug and cell line-cell line that also largely affect synergistic mechanisms. To this end, here we propose a multi-modal deep learning framework, termed MDNNSyn, which adequately applies multi-source information and trains multi-modal features to infer potential synergistic drug combinations. MDNNSyn extracts topology modality features by implementing the multi-layer hypergraph neural network on drug synergy hypergraph and constructs semantic modality features through similarity strategy. A multi-modal fusion network layer with gated neural network is then employed for synergy score prediction. MDNNSyn is compared to five classic and state-of-the-art prediction methods on DrugCombDB and Oncology-Screen datasets. The model achieves area under the curve (AUC) scores of 0.8682 and 0.9013 on two datasets, an improvement of 3.70% and 2.71% over the second-best model. Case study indicates that MDNNSyn is capable of detecting potential synergistic drug combinations.