Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.

中文翻译：

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PermuteDDS：用于药物协同预测的可置换特征融合网络

药物联合疗法在临床癌症治疗中显示出了前景。然而，由于潜在组合的空间巨大，即使通过高通量筛选，也很难通过实验鉴定出所有具有协同相互作用的药物组合。尽管许多药物协同预测的计算方法已被证明可以成功缩小这一空间，但有效融合药物对和细胞系特征仍然缺乏研究，阻碍了当前算法理解药物和细胞系之间的复杂相互作用。在本文中，我们提出了一种用于药物协同预测的可置换特征融合网络，名为 PermuteDDS。 PermuteDDS 将药物和细胞系的多种表示作为输入，并采用可置换融合机制来组合药物和细胞系特征。在实验中，PermuteDDS 在两个基准数据集上展示了最先进的性能。此外，按不同组织分组的独立测试集的结果表明，PermuteDDS 具有良好的泛化性能。我们相信 PermuteDDS 是识别协同药物组合的有效且有价值的工具。它可在 https://github.com/littlewei-lazy/PermuteDDS 上公开获取。首先，本文提出了一种用于预测药物协同作用的可置换特征融合网络，称为 PermuteDDS，它从多种药物表示和细胞系表示中提取不同的信息。其次，可置换融合机制通过整合不同通道的信息将药物和细胞系的特征结合起来，从而能够利用药物和细胞系之间的复杂关系。第三，比较和消融实验提供了 PermuteDDS 在预测药物间协同作用方面的功效的证据。