ArticleChemical science2022
Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- Beyond Structural Representations: A Semantic-Relational Graph Neural Network for Polypharmacy Side Effect Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Mol2Image: an enhanced DDI prediction framework leveraging drug molecular descriptors.BMC bioinformatics · 2026Article
- A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.BMC bioinformatics · 2026Article
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.Bioinformatics (Oxford, England) · 2026Article
- Addressing model overcomplexity in drug-drug interaction prediction with molecular fingerprints.Journal of cheminformatics · 2026Article
- Multi-to-uni modal knowledge transfer pre-training for molecular representation learning.Nature communications · 2026Article
- MHAFR-DDI: a multimodal hierarchical attention fusion and relation-aware architecture for drug-drug interaction event prediction.Briefings in bioinformatics · 2026Article
- Hgtsynergy: a transfer learning method for predicting anticancer synergistic drug combinations based on a drug-drug interaction heterogeneous graph.BMC bioinformatics · 2026Article
- Graph-based deep learning for drug-drug interaction prediction: a systematic review.Frontiers in chemistry · 2026Review
- CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction.PloS one · 2026Article
- A Domain Adaptive Interpretable Substructure-Aware Graph Attention Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- A Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Knowledge-aware contrastive heterogeneous molecular graph learning.PLoS computational biology · 2025Article
- A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs. In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism and a substructure-substructure interaction module (SSIM) for DDI prediction (SA-DDI). Specifically, the substructure attention was designed to capture size- and shape-adaptive substructures based on the chemical intuition that the sizes and shapes are often irregular for functional groups in molecules. DDIs are fundamentally caused by chemical substructure interactions. Thus, the SSIM was used to model the substructure-substructure interactions by highlighting important substructures while de-emphasizing the minor ones for DDI prediction. We evaluated our approach in two real-world datasets and compared the proposed method with the state-of-the-art DDI prediction models. The SA-DDI surpassed other approaches on the two datasets. Moreover, the visual interpretation results showed that the SA-DDI was sensitive to the structure information of drugs and was able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method improved the generalization and interpretation capability of DDI prediction modeling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.