ArticleBriefings in bioinformatics2023
Drug repositioning based on weighted local information augmented graph neural network.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 84 citations in OpenAlex.
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Discovering interpretable drug formulation behavior patterns via a mechanistic-augmented conditional variational autoencoder.Scientific reports · 2026Article
- GHF-ACL: A novel contrastive learning framework with multi-order graph structures for herb-disease association prediction.PLoS computational biology · 2026Article
- Pharmaco-therapeutic modulation of the Glo1-Nrf2-RAGE functional network in chronic inflammation: polyphenols in the context of current anti-inflammatory therapy.Inflammopharmacology · 2026Review
- Exploring anticancer drug structures through vertex based resolving parameters.Scientific reports · 2026Article
- Neurovascular Uncoupling in Alzheimer's and Parkinson's Diseases: Mechanisms and Therapeutic Strategies.Brain sciences · 2026Review
- Towards a hierarchical approach and scalable semi-local centrality for key node identification in weighted complex networks.Scientific reports · 2026Article
- From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing.bioRxiv : the preprint server for biology · 2026Article
- Differentiation of RNA-protein docking structures through molecular dynamics simulation and machine learning methods.Briefings in bioinformatics · 2026Article
- [Drug repositioning prediction based on dynamic feature learning on heterogeneous graphs].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Double-Duty Drugs: Repositioning Antipsychotics to Combat Bacterial Infections.CNS neuroscience & therapeutics · 2026Review
- A quantitative study of cytotoxic compounds using graph based descriptors and machine learning.Scientific reports · 2026Article
- Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention optimization.Frontiers in public health · 2026Article
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Mechanism-aware and safety-validated AI for drug repurposing: a critical review and translational pharmacology framework.Frontiers in pharmacology · 2026Review
- Heterogeneous biological graph convolutional network for drug-target interaction prediction.PloS one · 2026Article
- Cutting-edge optimized multi-source data fusion for trusted execution and management of blockchain transactions on the internet of medical things (IoMT) with machine learning.Scientific reports · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- A deep learning based framework for music-synchronized dance choreography with pose quantization and motion prediction for activity recognition.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.
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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.