Graph2MDA：一种用于预测微生物-药物关联的多模态变分图嵌入模型。

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations.

作者信息

Deng Lei, Huang Yibiao, Liu Xuejun, Liu Hui

机构信息

School of Computer Science and Engineering, Central South University, Changsha 410083, China.

School of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China.

出版信息

Bioinformatics. 2022 Jan 27;38(4):1118-1125. doi: 10.1093/bioinformatics/btab792.

DOI:10.1093/bioinformatics/btab792

PMID:34864873

Abstract

MOTIVATION

Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations.

RESULTS

In this article, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaning of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75-95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method.

AVAILABILITY AND IMPLEMENTATION

Source codes and preprocessed data are available at https://github.com/moen-hyb/Graph2MDA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

大量临床研究表明，生活在人体内的微生物与人类宿主密切相互作用，并参与调节药物疗效和药物毒性。微生物已成为抗菌药物开发的新靶点。因此，筛选微生物 - 药物关联对药物研发大有裨益。随着微生物基因组和药理学数据集的增加，我们迫切希望开发一种有效的计算方法来识别新的微生物 - 药物关联。

结果

在本文中，我们提出了一种新颖的方法Graph2MDA，通过使用变分图自动编码器（VGAE）来预测微生物 - 药物关联。我们基于微生物和药物的多种特征构建了多模态属性图，如分子结构、微生物基因序列和功能注释。以多模态属性图作为输入，训练VGAE以学习每个节点和整个图的信息丰富且可解释的潜在表示，然后使用深度神经网络分类器来预测微生物 - 药物关联。超参数分析和模型消融研究表明了我们模型的敏感性和鲁棒性。我们在三个独立数据集上评估了我们的方法，实验结果表明我们提出的方法优于六种现有的最先进方法。我们还探索了所学习的药物潜在表示的含义，发现药物呈现出明显的聚类模式，与药物ATC分类显著一致。此外，我们对两种微生物和两种药物进行了案例研究，发现75 - 95%的预测关联已在PubMed文献中报道。我们广泛的性能评估验证了我们提出方法的有效性。