VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

BMC Bioinformatics

Research

Shudong Wang¹  Mengjie Wu²  Yinfei Feng²  Yuanyuan Zhang²  Zengqian Deng² 

[1] School of Computer Science and Technology, China University of Petroleum, Qingdao, China;Yinfei Feng Qingdao University of Technology, Qingdao, China;

关键词: Drug-target interaction prediction;    Variational inference;    Graph autoencoder;    Variational expected maximum algorithm;    Drug repurposing;   

DOI  :  10.1186/s12859-023-05387-w

 received in 2023-04-10, accepted in 2023-06-16,  发布年份 2023

来源: Springer

【 摘 要 】

MotivationAccurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.ResultsTo solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

【 授权许可】

CC BY   
© The Author(s) 2023

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