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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-8879716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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