ArticlePloS one2014
Construction of drug network based on side effects and its application for drug repositioning.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 128 citations in OpenAlex.
- Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing.JCO clinical cancer informatics · 2019Pooled it
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- Integrated Artificial Intelligence and Physics-Based Methods for theMedicinal chemistry (Shariqah (United Arab Emirates)) · 2025Article
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- MicroRNA Guided In Silico Drug Repositioning for Malaria.Acta parasitologica · 2024Article
- CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases.Briefings in bioinformatics · 2024Article
- DRML-Ensemble: drug repurposing method based on feature construction of multi-layer ensemble.Journal of molecular modeling · 2024Article
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.Interdisciplinary sciences, computational life sciences · 2024Article
- Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks.Frontiers in pharmacology · 2024Article
- The OREGANO knowledge graph for computational drug repurposing.Scientific data · 2023Article
- A systems medicine strategy to predict the efficacy of drugs for monogenic epilepsies.Epilepsia · 2022Article
- RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.BMC bioinformatics · 2022Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.Frontiers in public health · 2022Article
- Evaluating the performance of drug-repurposing technologies.Drug discovery today · 2022Review
- Role of Traditional Chinese Medicine in Bone Regeneration and Osteoporosis.Frontiers in bioengineering and biotechnology · 2022Review
- Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug-Gene Interactions.Pharmaceutics · 2021Article
- Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review.International journal of molecular sciences · 2021Review
- Drug repurposing in Raynaud's phenomenon through adverse event signature matching in the World Health Organization pharmacovigilance database.British journal of clinical pharmacology · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of Normalized Discounted Cumulative Gain in the top 5 positions (NDCG@5) ≥ 0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5 ≥ 0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with Expression Analysis Systematic Explorer (EASE) score ≤ 10(-5) (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5 ≥ 0.7. In all of the 106 drug-indication pairs with an EASE score ≤ 0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.
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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.