DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

Yihan Zhao,Mengmeng Guo,Hua Qu,Yuhui Wang,Baoyi Guan,Lei Song,Jie Gao,Dazhuo Shi,Ying Zhang,Kai Zheng

doi:10.1186/s12967-020-02602-7

Abstract

BackgroundDrug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.MethodsA novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.ResultsThe experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.ConclusionsDLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

Highlights

Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development
As an overview (Fig. 1), DLDTI integrates learning from complex network’s various heterogeneous information to obtain low-dimensional and deep rich features (Fig. 2), through a processing method known as compact feature learning
DLDTI determines the optimal mapping from the plenary mapping space to the prediction subspace, and whether the feature vector is close to the known correlations

Summary

Introduction

The strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Research on drug development is becoming increasingly expensive, while the number of newly approved drugs per year remains quite low [1, 2]. In contrast to the classical hypothesis of “one gene, one drug, one disease”, drug repositioning aims to identify new characteristics of existing drugs [3]. A number of existing drugs have been successfully tuned to the new requirements. Galanthamine, an acetylcholinesterase inhibitor for treating paralysis, has been approved for Alzheimer’s disease [6]

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Discussion

Conclusion