Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

Journal: Journal of translational medicine

PMID: 38880914

Abstract

BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level.

Authors

Shi-Hui He

School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
Lijun Yun

School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China. yunlijun@ynnu.edu.cn.
Hai-Cheng Yi

Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China. yihaicheng17@mails.ucas.ac.cn.

Keywords

Computer Graphics Drug Combinations Drug Interactions Drug-Related Side Effects and Adverse Reactions Humans Neural Networks, Computer Reproducibility of Results Risk Assessment

External Resources

View on PubMed Access via DOI PubMed (38880914)

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

Abstract

Authors

Keywords

External Resources

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