NeuRank: learning to rank with neural networks for drug–target interaction prediction

BMC Bioinformatics

Xiujin Wu¹  Wenhua Zeng¹  Fan Lin¹  Xiuze Zhou² 

[1] School of Informatics, Xiamen University, Xiamen, China;Shuye Technology Co., Ltd., Hangzhou, China;

关键词: Drug–target interactions;    Drug discovery;    Neural network;    Ranking task;   

DOI  :  10.1186/s12859-021-04476-y

来源: Springer

【 摘 要 】

BackgroundExperimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified.ResultsWe treat the prediction of DTIs as a ranking problem and propose a neural network architecture, NeuRank, to address it. Also, we assume that similar drug compounds are likely to interact with similar target proteins. Thus, in our model, we add drug and target similarities, which are very effective at improving the prediction of DTIs. Then, we develop NeuRank from a point-wise to a pair-wise, and further to list-wise model.ConclusionFinally, results from extensive experiments on five public data sets (DrugBank, Enzymes, Ion Channels, G-Protein-Coupled Receptors, and Nuclear Receptors) show that, in identifying DTIs, our models achieve better performance than other state-of-the-art methods.

【 授权许可】

CC BY