Title: Deep graph embedding for prioritizing synergistic anticancer drug combinations Show Chinese title

Title: 深度图嵌入用于优先排序协同抗癌药物组合

Abstract: Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico. Show Chinese abstract

Abstract: 药物组合常用于治疗癌症患者，以提高疗效、减少不良副作用或克服耐药性。鉴于药物组合数量庞大，实验筛选所有可能的药物对既耗时又昂贵。目前，尚未充分探索利用最近开发的深度学习技术整合多个网络来预测协同药物组合。在本研究中，我们提出了一种图卷积网络（GCN）模型，以特别预测特定癌细胞系中的协同药物组合。具体而言，GCN方法使用卷积神经网络模型进行异构图嵌入，从而解决链接预测任务。本研究中的图是一个多模态图，通过整合药物-药物组合、药物-蛋白质相互作用和蛋白质-蛋白质相互作用网络构建而成。我们发现，GCN模型能够从大型异构网络中正确预测细胞系特异性的协同药物组合。39个细胞系特异性模型中，大多数（30个）的受试者工作特征曲线下的面积（AUC）大于0.80，平均AUC为0.84。此外，我们进行了深入的文献调查，以研究特定癌细胞系中预测效果最好的药物组合，并发现其中许多组合已被发现表现出对相同或其它癌症的协同抗肿瘤活性，无论是体外还是体内。综上所述，结果表明，我们的研究提供了一种有前景的方法，以更好地在计算机上预测和优化协同药物对。