基于关系融合和双向扩散模型的药物与靶标关系预测方法研究

Predicting Drug-Target Relationship Based on Relation Fusion and Bidirectional Mass Diffusion Model

【目的】提出了一种新的药物与靶标关系预测方法来提升预测性能。【方法】进一步丰富网络的语义信息,采用SNF、AVG和MAX方法分别对药物相似性网络和靶标相似性网络中的多种语义关系进行融合。基于关系融合后的相似性网络和已知的药物-靶标互作网络构建双向扩散模型,以实现药物与靶标关系预测。【结果】实证研究表明,本文方法相较于主流的预测方法在AUC值指标上分别提升了2.2%和12.8%。并且通过对预测结果进行文献回溯,预测分数排在前10、20和30位的药物-靶标关系对中,可以分别在文献中找到3、8和11对药物-靶标的相关线索与证据。另外,SNF的融合效果最优,能够最大程度提高预测的性能。【局限】未融合药物或靶标客观属性上的相似性,如药物的化学结构或靶标的序列结构相似性,并且针对新药物和新靶标关系发现的冷启动问题仍待解决。【结论】本文提出的预测方法具有一定的优越性和有效性,可以为药物重定位以及其他生物医学实体的关系预测相关研究提供参考。

[Objective]This study proposes a new method to predict the relationship between drugs and targets to improve the prediction performance.[Methods]Firstly,we used the SNF,AVG,and MAX methods to fuse multiple semantic relationships in drug and target similarity networks,which further enriched the semantic information of the networks.Then,we constructed a bidirectional diffusion model based on the fused similarity networks and the existing drug-target interaction network to predict the drug-target relationship.[Results]Compared with mainstream forecasting models,our method’s AUC value index improved by 2.2%and 12.8%.With a retrospective study,the prediction scores ranked in the top 10,20,and 30 drug-target relationship pairs,and clues and evidence related to 3,8,and 11 drug-target pairs could be found in the literature.The SNF had the best fusion effect and maximized the prediction.[Limitations]We did not fuse similarities in objective attributes of drugs or targets,such as the chemical structure of drugs or sequence structure similarities of targets.The cold start problem in the relationship between new drugs and new targets still needs to be solved.[Conclusions]The prediction method proposed in this study could provide some references for the research on drug repositioning and relationship prediction of other biomedical entities.