Drug-Drug Interaction Prediction
Dive into the latest advances in predicting drug-drug interactions, a crucial aspect of pharmaceutical research, through this collection of studies employing cutting-edge graph neural networks and deep learning methodologies.
- Modeling Polypharmacy Side Effects with Graph Convolutional Networks
- DeepDrug: A General Graph-Based Deep Learning Framework for Drug-Drug Interactions and Drug-Target Interactions Prediction
- KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
- DeepDDS: Deep Graph Neural Network with Attention Mechanism to Predict Synergistic Drug Combinations
- By: Wang, J. et al., 2022
- Journal: Briefings in Bioinformatics
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- A Dual Graph Neural Network for Drug-Drug Interactions Prediction Based on Molecular Structure and Interactions
- By: Ma, M. & Lei, X., 2023
- Journal: PLOS Computational Biology
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- Prediction of Drug-Drug Interaction Events Using Graph Neural Networks Based Feature Extraction
- By: Al-Rabeah, M. H. & Lakizadeh, A., 2022
- Journal: Scientific Reports - Nature
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- A Biomedical Knowledge Graph-Based Method for Drug-Drug Interactions Prediction Through Combining Local and Global Features with Deep Neural Networks
- By: Ren, Z. H. et al., 2022
- Journal: Briefings in Bioinformatics
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- Learning Size-Adaptive Molecular Substructures for Explainable Drug-Drug Interaction Prediction by Substructure-Aware Graph Neural Network
- Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks
- By: Liu, S. et al., 2022
- Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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- SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
- 3DGT-DDI: 3D graph and text based neural network for drug–drug interaction prediction
- Multi-type feature fusion based on graph neural network for drug-drug interaction prediction