ArticleScientific data2017
A standard database for drug repositioning.
Article in Scientific data, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
141 citing papers in PubMed.
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- Article
- A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions.bioRxiv : the preprint server for biology · 2026Article
- ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.Scientific reports · 2026Article
- A fused deep learning approach to transform drug repositioning.Communications chemistry · 2025Article
- Improving computational drug repositioning through multi-source disease similarity networks.Scientific reports · 2025Article
- Literature data-based de novo candidates for drug repurposing.BMC bioinformatics · 2025Article
- CPDP: Contrastive Protein-Drug Pre-Training for Novel Drug Discovery.International journal of molecular sciences · 2025Article
- Influence of statin intervention on peripheral neuropathy in patients treated with anticancer drugs identified from the insurer database.Journal of pharmaceutical health care and sciences · 2025Article
- Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery.Scientific reports · 2025Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- Storage and Query of Drug Knowledge Graphs Using Distributed Graph Databases: A Case Study.Bioengineering (Basel, Switzerland) · 2025Article
- Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.Scientific reports · 2025Article
- Targeting SLC4A4: A Novel Approach in Colorectal Cancer Drug Repurposing.Current issues in molecular biology · 2025Article
- On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning.Scientific data · 2025Article
- From Lab to Clinic: Success Stories of Repurposed Drugs in Treating Major Diseases.Advances in pharmacological and pharmaceutical sciences · 2025Review
- Mechanisms to medicines: navigating drug repurposing strategies in Alzheimer's disease.Frontiers in aging neuroscience · 2025Review
- Drug functional remapping: a new promise for tumor immunotherapy.Frontiers in oncology · 2025Review
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
81 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).
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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.