HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction
Abstract Background Accurate identification of drug-drug interactions (DDIs) is critical in pharmacology, as DDIs can either enhance therapeutic efficacy or trigger adverse reactions when multiple medications are administered concurrently. Traditional methods for identifying DDIs are labor-intensive...
Saved in:
| Main Authors: | Yue Luo, Lei Deng, Zhijian Huang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-025-06157-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning
by: Jinchen Sun, et al.
Published: (2025-01-01) -
DDI-KGAT: A Graph Attention Network on Biomedical Knowledge Graph for the Prediction of Drug-Drug Interactions
by: Iqra Naseer Kundi, et al.
Published: (2024-01-01) -
MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions
by: Xiang Li, et al.
Published: (2024-01-01) -
Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks
by: Bin Yang, et al.
Published: (2025-05-01) -
Enhanced Attention-Driven Dynamic Graph Convolutional Network for Extracting Drug-Drug Interaction
by: Xiechao Guo, et al.
Published: (2025-02-01)