SmileGNN: Drug-Drug Interaction Prediction Based on the SMILES and Graph Neural Network

Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug-drug interactions widely and effectively before the drugs enter the market. Therefore, the...

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Bibliographic Details
Published in:Life (Basel, Switzerland) Switzerland), 2022-02, Vol.12 (2), p.319
Main Authors: Han, Xueting, Xie, Ruixia, Li, Xutao, Li, Junyi
Format: Article
Language:English
Subjects:
Accuracy
Clinical trials
Deep learning
Drug interaction
Drug interactions
Drugs
drug–drug interaction prediction
graph neural network
Graph neural networks
Graph representations
Graph theory
knowledge graph
Knowledge representation
Medical research
Neural networks
Ontology
Prediction models
Resource Description Framework-RDF
Side effects
structural features
topological features
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Summary:Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug-drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug-drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug-drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug-drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug-drug interactions. Five out of the top ten predicted new drug-drug interactions are verified from the latest database, which proves the credibility of SmileGNN.
ISSN:2075-1729
2075-1729
DOI:10.3390/life12020319