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. 2019 Jul 24;20(Suppl 13):383.
doi: 10.1186/s12859-019-2858-6.

Drug repurposing with network reinforcement

Yonghyun Nam  1 , Myungjun Kim  1 , Hang-Seok Chang  2 , Hyunjung Shin  3
Affiliations

Drug repurposing with network reinforcement

Yonghyun Nam et al. BMC Bioinformatics. .

Abstract

Background: Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data.

Results: The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations.

Conclusions: The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

Keywords: Drug repurposing; Drug scoring; Network reinforcement; Semi-supervised learning.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The main idea of drug repurposing with network reinforcement: Red circle represents the originally known drugs, and blue circle represents isolated (disconnected) drugs from the drug network. The center panel shows the drug network constructed by drug-protein association and its reinforced network with additional information. Left and right panel show the scoring results according to (a) and (b), respectively
Fig. 2
Fig. 2
Experimental setting for measuring prediction performance: (a) a drug network constructed with five drugs with target disease D2. If dr2 and dr4 are already in use for treating D2, we randomly select one and assign label ‘1’ (b) validation of the SSL result for non-selected (but already in use) dr4
Fig. 3
Fig. 3
Performance improvement of the enhanced network: the gray and blue bar represents histogram of AUC for the original and enhanced network, respectively. The histogram is shifted towards right for the enhanced network. In addition, the inserts summarize overall average AUC with deviation. The p-value for statistical tests for pairwise comparison between original network-reinforce network and data fusion network-reinforce network are 0.0003 and 0.0005, respectively
Fig. 4
Fig. 4
A snapshot of the enhanced drug network: the solid lines represent original connections with information on shared target protein, and the dotted lines represent newly connected edges using CLASH. Red circles represent orphan drugs but linked to the network by the proposed method. For better readability, the network is simplified. More detailed one is provided in Additional file 1
Fig. 5
Fig. 5
Enhanced drug network focused on vascular dementia: (a) The network is divided into three regions, in which the 1st tier region around the center consist of drugs with score value greater than 0.9. (b) Left panel shows the drug scoring results using original network. Right panel shows the proposed scoring results using enhanced network
Fig. 6
Fig. 6
Drug repurposing with network reinforcement: The proposed method has three steps; (a) drug network construction based on drug-target protein, (b) network reinforcement with external knowledge such as bioactivity, medications, etc., and (c) drug scoring with SSL for a specific disease based on the enhanced network
See this image and copyright information in PMC

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