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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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Detalles Bibliográficos
Autores principales: Han, Xueting, Xie, Ruixia, Li, Xutao, Li, Junyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879716/
https://www.ncbi.nlm.nih.gov/pubmed/35207606
http://dx.doi.org/10.3390/life12020319
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author Han, Xueting
Xie, Ruixia
Li, Xutao
Li, Junyi
author_facet Han, Xueting
Xie, Ruixia
Li, Xutao
Li, Junyi
author_sort Han, Xueting
collection PubMed
description 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.
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spelling pubmed-88797162022-02-26 SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network Han, Xueting Xie, Ruixia Li, Xutao Li, Junyi Life (Basel) Article 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. MDPI 2022-02-21 /pmc/articles/PMC8879716/ /pubmed/35207606 http://dx.doi.org/10.3390/life12020319 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Xueting
Xie, Ruixia
Li, Xutao
Li, Junyi
SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
title SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
title_full SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
title_fullStr SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
title_full_unstemmed SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
title_short SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
title_sort smilegnn: drug–drug interaction prediction based on the smiles and graph neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879716/
https://www.ncbi.nlm.nih.gov/pubmed/35207606
http://dx.doi.org/10.3390/life12020319
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