ReviewCurrent drug research reviews2026
Repositioning Drugs: A Computational Approach.
Review in Current drug research reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- When Side Effects Become Therapies: A Mechanistic Taxonomy of Adverse Drug Reactions as Translational Signals for Drug Repositioning.Fundamental & clinical pharmacology · 2026Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational drug repositioning has emerged as an efficient approach to discovering new indications for existing drugs, offering lower risk and cost compared to traditional drug discovery methods. Various computational approaches have been developed, including targetbased, gene-expression-based, phenome-based, and multi-omics-based methods. Recent advancements leverage diverse data sources, such as biomedical databases and online healthrelated information. Techniques incorporating drug structure and target information have shown promising results in predicting new drug indications. Despite significant progress, challenges remain, including data noise reduction, method ensemble, negative sample selection, and data sparseness. Overall, computational drug repositioning continues to be a valuable tool in drug discovery and development.
Indexed as
Identifiers
40264316What 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.