deepDR: a network-based deep learning approach to in silico drug repositioning

Abstract

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.

Fig. 1.

Pipeline of deepDR. (a) deepDR generates random walk-based network representation from a complicated heterogeneous network that contains 10 drug-related networks (see Section 2). (b) deepDR fuses PPMI (positive pointwise mutual information) matrices of each network into a compact, low-dimensional feature representation common to all networks via a multi-modal deep autoencoder (MDA), low-dimensional features are then extracted from the middle layer of the MDA. (c) deepDR uses a collective variational autoencoder (cVAE) to predict potential associations between drugs and diseases. Drug features and known (clinically reported or approved) drug–disease interactions are encoded and decoded collectively by the same inference network and generation network

Fig. 2.

Performance of different methods on the clinically reported drug–disease network. (a) Receiver operating characteristic (ROC) curves of prediction results obtained by applying deepDR and six previously reported methods in 5-fold cross-validation. (b) Precision–recall (PR) curves of prediction results obtained by applying deepDR and other competitive methods in 5-fold cross-validation. (c) Recall of deepDR and other methods against top k predicted list during 5-fold cross-validation

Fig. 3.

Evaluation of deepDR on the external validation set collected from the ClinialTrial.gov database (see Section 2). (a) Receiver operating characteristic (ROC) curves of prediction results obtained by applying deepDR and other competitive methods. (b) Recall against top k predicted list in the external validation

Fig. 4.

Performance of MDA when comparing with different network representation approaches. (a) Receiver operating characteristic (ROC) curves of prediction results obtained by applying deepDR and other methods. (b) Precision–recall (PR) curves of prediction results obtained by applying deepDR and other methods

Fig. 5.

Performance of cVAE when comparing with different traditional classifiers. (a) Receiver operating characteristic (ROC) curves of prediction results obtained by comparing deepDR with other methods. (b) Precision–recall (PR) curves of prediction results obtained by comparing deepDR with other methods