Drug repurposing and prediction of multiple interaction types via graph embedding

• Published in: BMC bioinformatics Vol. 24; no. 1; p. 202
• Main Authors: Amiri Souri, E, Chenoweth, A, Karagiannis, S N, Tsoka, S
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 17.05.2023; BioMed Central; BMC
• Subjects: Analysis; Biomarkers; Cancer therapies; Classification; Datasets; Disease; Drug approval; Drug discovery; Drug Discovery - methods; Drug interaction; Drug Interactions; Drug Repositioning; Drug repurposing; Drug-target interaction; Drugs; Embedding; Genes; Graph theory; Health aspects; Knowledge; Ligands; Machine learning; Network embedding; Pharmaceutical research; Predictions; Proteins; R&D; Research & development; United Kingdom; Drug repurposing; Network embedding; Drug discovery; Drug-target interaction; Machine learning
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05317-w

Finding 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. A 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. DT2Vec+ 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.