3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction

ASIAIR > College of Computer Science > Department of Biomedical informatics > Journal Article > Item 310904400/114996

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/114996

Title:	3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction
Authors:	He, Haohuai;He, Haohuai;Che, Guanxing;Chen, Guanxing;陳語謙
Contributors:	資訊電機學院生物資訊與醫學工程學系
Keywords:	Drug-Drug-interaction: 3D Graph Neural Network: Explainability: Attention-Mechanism
Date:	2022-05-01
Issue Date:	2023-03-28 01:55:45 (UTC+0)
Publisher:	亞洲大學
Abstract:	Motivation Drug–drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificial intelligence methods predict and mine potential DDI, they ignore the 3D structure information of drug molecules and do not fully consider the contribution of molecular substructure in DDI. Results We proposed a new deep learning architecture, 3DGT-DDI, a model composed of a 3D graph neural network and pre-trained text attention mechanism. We used 3D molecular graph structure and position information to enhance the prediction ability of the model for DDI, which enabled us to deeply explore the effect of drug substructure on DDI relationship. The results showed that 3DGT-DDI outperforms other state-of-the-art baselines. It achieved an 84.48% macro F1 score in the DDIExtraction 2013 shared task dataset. Also, our 3D graph model proves its performance and explainability through weight visualization on the DrugBank dataset. 3DGT-DDI can help us better understand and identify potential DDI, thereby helping to avoid the side effects of drug mixing.
Appears in Collections:	[Department of Biomedical informatics ] Journal Article

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