ArticleFrontiers in pharmacology2022
Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Comparative Performance Evaluation of GCN, GAT, and GraphSAGE Architectures for Drug-Gene Interaction Prediction in Ameloblastoma via MEK-Pathway Targeting.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- BioMNEDR: mechanism-guided network embedding for drug repurposing.Briefings in bioinformatics · 2026Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- Article
- Article
- The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?Molecules (Basel, Switzerland) · 2024Review
- GCARDTI: Drug-target interaction prediction based on a hybrid mechanism in drug SELFIES.Quantitative biology (Beijing, China) · 2024Article
- StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.PLoS computational biology · 2024Article
- Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.Interdisciplinary sciences, computational life sciences · 2024Article
- CAEM-GBDT: a cancer subtype identifying method using multi-omics data and convolutional autoencoder network.Frontiers in bioinformatics · 2024Article
- Review
- MSResG: Using GAE and Residual GCN to Predict Drug-Drug Interactions Based on Multi-source Drug Features.Interdisciplinary sciences, computational life sciences · 2023Article
- Fragment-pair based drug molecule solubility prediction through attention mechanism.Frontiers in pharmacology · 2023Article
- Drug repositioning based on heterogeneous networks and variational graph autoencoders.Frontiers in pharmacology · 2022Article
- Graph attention network predicts drug-gene associations of matrix metalloproteinases 9-based host modulation in periodontitis.Journal of Indian Society of PeriodontologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug-drug interaction network and disease similarity network are constructed according to the drug-gene and disease-gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found
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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.