ArticlePLoS computational biology2023
A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Deterministic and stochastic interventions in reducing drug-drug interactions in inappropriate prescribing: A systematic review.PloS one · 2026Pooled it
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- A dynamic fusion diffusion model for molecular generation.Molecular diversity · 2026Article
- Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Transformer-based graphs for drug-drug interaction with chemical knowledge embedding.Briefings in bioinformatics · 2026Article
- InfoMedex: drug-drug interaction prediction via a multimodal CNN-transformer model.Frontiers in drug safety and regulation · 2026Article
- CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction.PloS one · 2026Article
- Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction.PloS one · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- MolCL-SP: a multimodal contrastive learning framework with non-overlapping substructure perturbations for molecular property prediction.Bioinformatics (Oxford, England) · 2025Article
- LSA-DDI: Learning Stereochemistry-Aware Drug Interactions via 3D Feature Fusion and Contrastive Cross-Attention.International journal of molecular sciences · 2025Article
- DrIVeNN: Drug Interaction Vectors Neural Network.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- SCATrans: semantic cross-attention transformer for drug-drug interaction predication through multimodal biomedical data.BMC bioinformatics · 2025Article
- HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- Identify drug-drug interactions via deep learning: A real world study.Journal of pharmaceutical analysis · 2025Article
- Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks.Scientific reports · 2025Article
- Knowledge-aware contrastive heterogeneous molecular graph learning.PLoS computational biology · 2025Article
- Predicting rare drug-drug interaction events with dual-granular structure-adaptive and pair variational representation.Nature communications · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug's unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN-DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.
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