ReviewBriefings in bioinformatics2016
A survey of current trends in computational drug repositioning.
Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03269474 (Computational Drug Repurposing for All Epidermolysis Bullosa Simplex), which is not on this map. Cited by 241 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Computational Drug Repurposing for All Epidermolysis Bullosa Simplex (EBS) Cases
Who cites it
241 citing papers in PubMed, 1 synthesis or guideline pooled it, 573 citations in OpenAlex.
- A review of validation strategies for computational drug repositioning.Briefings in bioinformatics · 2018Pooled it
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- Contextual Representation Learning With ResNet Refinement and Deep Classification for Enhanced Drug Target Interaction Prediction.Chemical biology & drug design · 2026Article
- Virtual screening of compounds for the development of thyroid hormone analogues for potential application in cardiac regeneration.Journal of computer-aided molecular design · 2026Article
- Drug repurposing: a promising drug discovery strategy for the treatment of emerging epidemic infectious disease.Molecular diversity · 2026Review
- TheraMind: a multi-LLM ensemble for accelerating drug repurposing in lung cancer via case report mining.NPJ precision oncology · 2026Article
- Leveraging bioinformatics approaches for drug repositioning in space radiation protection.PloS one · 2026Article
- Integrative Network-Based Transcriptomic Analysis Identifies Niclosamide as a Candidate Repositioned Drug for Breast Cancer.Breast cancer (Dove Medical Press) · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Transcriptome-Guided Drug Repurposing Identifies Homoharringtonine (HHT) as a Candidate for Radiation-Induced Pulmonary Fibrosis.Pharmaceutics · 2025Article
- Repurposing Acebutolol for Osteoporosis Treatment: Insights From Multi-Omics and Multi-Modal Data Analysis.Clinical pharmacology and therapeutics · 2025Article
- Graph convolution network based on meta-paths and mutual information for drug-target interaction prediction.BMC bioinformatics · 2025Article
- DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.Briefings in bioinformatics · 2025Article
- MDG-DDI: multi-feature drug graph for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.Interdisciplinary sciences, computational life sciences · 2025Article
- Dual-route embedding-aware graph neural networks for drug repositioning.Briefings in bioinformatics · 2025Article
- Computational drug repurposing: approaches, evaluation of in silico resources and case studies.Nature reviews. Drug discovery · 2025Review
- HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.BMC bioinformatics · 2025Article
- Signature-based repurposed drugs resemble the inhibition of TGFβ-induced NDRG1 as potential therapeutics for triple-negative breast cancer.International journal of biological sciences · 2025Article
181 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
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.
Indexed as
Identifiers
What OpenQuestion holds
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.