Drug repurposing and prediction of multiple interaction types via graph embedding

BMC Bioinformatics

Research

E. Amiri Souri¹  S. Tsoka¹  A. Chenoweth²  S. N. Karagiannis² 

[1] Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King’s College London, Bush House, WC2B 4BG, London, UK;St. John’s Institute of Dermatology, School of Basic and Medical Biosciences, Guy’s Hospital, King’s College London, SE1 9RT, London, UK;Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Guy’s Cancer Centre, King’s College London, SE1 9RT, London, UK;

关键词: Drug-target interaction;    Drug discovery;    Drug repurposing;    Network embedding;    Machine learning;   

DOI  :  10.1186/s12859-023-05317-w

 received in 2023-02-22, accepted in 2023-04-30,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundFinding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug–target links, as well as delineating the type of drug interaction, are important in drug repurposing studies.ResultsA computational drug repurposing approach was proposed to predict novel drug–target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug–drug and protein–protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers.ConclusionDT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug–target–disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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