Drug repositioning based on the heterogeneous information fusion graph convolutional network
Drug repositioning based on the heterogeneous information fusion graph convolutional network
复制标题
基于异质信息融合图卷积网络的药物定位
DOI：
10.1093/bib/bbab319
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发表时间：
2021-08-10
影响因子：
9.5
通讯作者：
Su, Yansen
中科院分区：
文献类型：
--
作者：
Cai, Lijun;Lu, Changcheng;Su, Yansen
在计算机上重复使用旧药物（也称为药物重新定位）来治疗常见和罕见疾病正日益成为一个有吸引力的主张，因为它涉及使用降低风险的药物，可能降低总体开发成本和缩短开发时间。因此，迫切需要计算药物再利用方法来促进药物发现。在这项研究中，我们提出了一种新的方法，称为DRHGCN（基于异构信息融合图卷积网络的药物重新定位），以发现针对某种疾病的潜在药物。为了充分利用不同领域（即药物-药物相似性，疾病-疾病相似性和药物-疾病关联网络）的不同拓扑信息，我们首先通过对网络应用图卷积运算来学习药物和疾病的嵌入来设计域间和域内特征提取模块，而不是简单地将三个网络集成为异构网络。然后，我们并行融合域间和域内嵌入，以获得更有代表性的药物和疾病嵌入。最后，我们引入了一个层注意机制来联合收割机从多个图卷积层嵌入，以进一步提高预测性能。我们发现DRHGCN在四个基准数据集中实现了高性能（平均AUROC为0.934，平均AUPR为0.539），优于当前的方法。重要的是，我们对DRHGCN预测的候选药物进行了分子对接实验，为阿尔茨海默病（例如苯扎托品）和帕金森病（例如苯海索和氟哌啶醇）提供了几种新的批准药物。
In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).