MSEDDI: Multi-Scale Embedding for Predicting Drug—Drug Interaction Events

MSEDDI: Multi-Scale Embedding for Predicting Drug—Drug Interaction Events

A norm in modern medicine is to prescribe polypharmacy to treat disease. The core concern with the co-administration of drugs is that it may produce adverse drug—drug interaction (DDI), which can cause unexpected bodily injury. Therefore, it is essential to identify potential DDI. Most existing meth...

Full description

Bibliographic Details
Main Authors:	Liyi Yu, Zhaochun Xu, Meiling Cheng, Weizhong Lin, Wangren Qiu, Xuan Xiao
Format:	Article
Language:	English
Published:	MDPI AG 2023-02-01
Series:	International Journal of Molecular Sciences
Subjects:	drug—drug interaction knowledge graph graph neural network self-attention mechanism
Online Access:	https://www.mdpi.com/1422-0067/24/5/4500

Similar Items

HGDTI: predicting drug–target interaction by using information aggregation based on heterogeneous graph neural network
by: Liyi Yu, et al.
Published: (2022-04-01)

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
by: Yue-Hua Feng, et al.
Published: (2022-05-01)

SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
by: Xueting Han, et al.
Published: (2022-02-01)

Predicting drug–protein interactions by preserving the graph information of multi source data
by: Jiahao Wei, et al.
Published: (2024-01-01)

A novel method for drug-target interaction prediction based on graph transformers model
by: Hongmei Wang, et al.
Published: (2022-11-01)

iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development
by: Xiang Cheng, et al.
Published: (2022-12-01)

A novel drug-drug interactions prediction method based on a graph attention network
by: Xian Tan, et al.
Published: (2023-08-01)

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions
by: Jing Zhang, et al.
Published: (2023-02-01)

DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction
by: Jie Yang, et al.
Published: (2023-04-01)

Comparing methods for drug–gene interaction prediction on the biomedical literature knowledge graph: performance versus explainability
by: Fotis Aisopos, et al.
Published: (2023-06-01)

Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method
by: Xingjian Xu, et al.
Published: (2023-01-01)

Understanding the performance of knowledge graph embeddings in drug discovery
by: Stephen Bonner, et al.
Published: (2022-12-01)

Molecular Substructure-Aware Network With Reinforcement Pooling and Deep Attention Mechanism for Drug-Drug Interaction Prediction
by: Ke Zhao, et al.
Published: (2024-01-01)

MultiGML: Multimodal graph machine learning for prediction of adverse drug events
by: Sophia Krix, et al.
Published: (2023-09-01)

Co-Attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
by: Junhyun Lee, et al.
Published: (2023-01-01)

An Improved Graph Isomorphism Network for Accurate Prediction of Drug–Drug Interactions
by: Sile Wang, et al.
Published: (2023-09-01)

MFDA: Multiview fusion based on dual-level attention for drug interaction prediction
by: Kaibiao Lin, et al.
Published: (2022-10-01)

GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity
by: Haelee Bae, et al.
Published: (2022-12-01)

Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion
by: Dawei Pan, et al.
Published: (2024-02-01)

AMMVF-DTI: A Novel Model Predicting Drug–Target Interactions Based on Attention Mechanism and Multi-View Fusion
by: Lu Wang, et al.
Published: (2023-09-01)

A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug–Target Interaction Prediction
by: Liwei Liu, et al.
Published: (2023-09-01)

DDI-SSL: Drug–Drug Interaction Prediction Based on Substructure Signature Learning
by: Yuan Liang
Published: (2023-09-01)

SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
by: Shugang Zhang, et al.
Published: (2021-08-01)

BioChemDDI: Predicting Drug–Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism
by: Zhong-Hao Ren, et al.
Published: (2022-05-01)

DPDDI: a deep predictor for drug-drug interactions
by: Yue-Hua Feng, et al.
Published: (2020-09-01)

A Message Passing Approach to Biomedical Relation Classification for Drug–Drug Interactions
by: Dimitrios Zaikis, et al.
Published: (2022-10-01)

Prediction of adverse drug reactions based on knowledge graph embedding
by: Fei Zhang, et al.
Published: (2021-02-01)

GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures
by: Shengtian Sang, et al.
Published: (2019-01-01)

Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism
by: Mingxuan Che, et al.
Published: (2021-01-01)

EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
by: Yuan Jin, et al.
Published: (2021-11-01)

A GraphSAGE-based model with fingerprints only to predict drug-drug interactions
by: Bo Zhou, et al.
Published: (2024-01-01)

Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding
by: Jiaxin Li, et al.
Published: (2022-08-01)

Multi-view feature representation and fusion for drug-drug interactions prediction
by: Jing Wang, et al.
Published: (2023-03-01)

Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive Learning
by: Liwei Jiang, et al.
Published: (2023-10-01)

Drug repositioning of COVID-19 based on mixed graph network and ion channel
by: Xianfang Wang, et al.
Published: (2022-01-01)

Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction
by: Shihui He, et al.
Published: (2024-02-01)

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications
by: Mona Alshahrani, et al.
Published: (2022-04-01)

Augmenting Embedding Projection With Entity Descriptions for Knowledge Graph Completion
by: Junfan Chen, et al.
Published: (2021-01-01)

DeepNC: a framework for drug-target interaction prediction with graph neural networks
by: Huu Ngoc Tran Tran, et al.
Published: (2022-05-01)

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
by: Maha A. Thafar, et al.
Published: (2020-06-01)