ArticleFrontiers in molecular biosciences2026
A network-driven computational framework for identifying FDA-approved drug repurposing across heterogeneous brain cancers.
Article in Frontiers in molecular biosciences, 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.
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Brain cancers are notorious for their heterogeneity, which complicates therapeutic decisions because of recurrently dysregulated signaling pathways. Cancer system characterizations have allowed the identification of some key components involved in brain cancer, such as the epidermal growth factor receptor, Methods: In the present study, a protocol was designed to address this complexity using the identified pathway components. These components served as the basis for defining the signatures of small molecules. The set of molecular signatures was then used to develop a network-driven computational framework. Accordingly, two in-house applications were developed, namely, a molecular profile generator called " Results: A total of 2,809 FDA-approved drug molecules (molecular weight ≤500 Da) were profiled using Conclusion: The proposed profile-network-based method achieved 70%-95% accuracy for drug repurposing across different disease categories related to the brain.
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.