Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug–drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein–protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein–protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

中文翻译：

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HANSynergy：用于药物协同预测的异构图注意网络


药物协同治疗是一种有前景的癌症治疗策略。然而，可用药物的种类繁多以及通过临床试验确定有效药物组合的耗时过程带来了重大挑战。它需要一种可靠的方法来快速、精确地选择药物协同作用。作为回应，已经开发了各种计算策略来预测药物协同作用，但异质生物网络特征的利用仍未得到充分探索。在这项研究中，我们利用 DrugCombDB、PubChem、UniProt 和癌细胞系百科全书 (CCLE) 等来源的丰富数据集构建了一个包含不同生物实体和相互作用的异质图。我们初始化节点特征表示并引入一种新颖的虚拟节点来增强药物表示。我们提出的方法，用于药物-药物协同预测的异构图注意网络（HANSynergy），经过实验验证，证明异构图注意网络可以提取关键节点特征，有效利用信息的多样性，并通过以下方式进一步增强网络功能：结合多头注意力机制。在对比实验中，DrugCombDB_early数据集的最高准确度（Acc）和曲线下面积（AUC）分别为0.877和0.947，证明了HANSynergy相对于竞争方法的优越性。此外，蛋白质-蛋白质相互作用对于理解药物的作用机制很重要。异质注意力机制有利于蛋白质-蛋白质相互作用分析。 通过分析异构网络训练前后注意力权重的变化，我们研究了可能与药物组合相关的蛋白质。此外，案例研究使我们的发现与现有研究保持一致，强调了 HANSynergy 在药物协同预测方面的潜力。这一进展不仅有助于药物协同预测的新兴领域，而且有可能提供有价值的见解并发现抗击癌症的新药物协同作用。