MOTIVATION Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. RESULTS In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide). AVAILABILITY AND IMPLEMENTATION Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. SUPPLEMENTARY INFORMATION Supplementary data are available online at Bioinformatics.

中文翻译：

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deepDR：一种基于网络的深度学习方法，用于计算机药物重新定位。

动机传统药物的发现和开发通常是耗时且高风险的。重新利用/重新放置已批准的药物为快速开发有效的治疗方法提供了一种相对低成本和高效的方法。大规模，异构生物网络的出现为开发计算机药物重新定位方法提供了前所未有的机会。然而，通过大多数现有的药物重定位方法来捕获高度非线性的异构网络结构一直是具有挑战性的。结果在这项研究中，我们通过整合10种网络：一种药物疾病，一种药物副作用，一种药物靶标和7种药物，开发了一种基于网络的深度学习方法，称为deepDR，用于计算机药物重用网络。具体来说，deepDR通过多模式深度自动编码器从异构网络中学习药物的高级功能。然后，通过变式自动编码器对学习到的药物的低维表示以及临床报告的药物-疾病对进行统一编码和解码，以推断出最初未被批准的已批准药物的候选对象。我们发现deepDR显示出高性能[接收器工作特性曲线下的面积（AUROC）= 0.908]，优于传统的基于网络或基于机器学习的方法。重要的是，可通过ClinicalTrials.gov数据库（AUROC = 0.826）验证deepDR预测的药物-疾病关联，并且我们展示了针对阿尔茨海默氏病（例如利培酮和阿立哌唑）和帕金森氏病（例如，epiperidone和阿立哌唑）的几种新颖的deepDR预测批准的药物。哌醋甲酯和培高利特）。可用性和实现可以从https://github.com/ChengF-Lab/deepDR下载源代码和数据。补充信息补充数据可从Bioinformatics在线获得。