SmileGNN: Drug-Drug Interaction Prediction Based on the SMILES and Graph Neural Network.
Life (Basel)
; 12(2)2022 Feb 21.
Article
em En
| MEDLINE
| ID: mdl-35207606
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Life (Basel)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China