Abstract：
The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.
摘要：
药物-药物相互作用（DDI）的鉴定在药物开发的各个领域中起着至关重要的作用。在这项研究中,提出了一种深度学习框架（KGCN_NFM），以使用耦合知识图卷积网络（KGCN）与神经分解机（NFM）来识别DDI。KGCN用于学习知识图（KG）中包含高阶结构信息和语义信息的嵌入表示。然后将药物的嵌入和Morgan分子指纹作为NFM的输入来预测DDI。基于两个不同大小的实际数据集，对当前方法的性能和有效性进行了评估和确认，结果表明，KGCN_NFM优于最先进的算法。此外，KGCN_NFM确定的托泊替康和丹琼之间的相互作用通过MTT试验进行了验证，凋亡实验，细胞周期分析,和分子对接。我们的研究表明，两种药物的联合治疗具有协同抗癌作用，为肺癌提供了有效的治疗策略。这些结果表明，KGCN_NFM是整合异构信息以识别潜在DDI的有价值的工具。
