Evidence map›Paper›PMID 41210919›Full record

ArticleFrontiers in bioinformatics2025

Drug repositioning pipeline integrating community analysis in drug-drug similarity networks and automated ATC community labeling to foster molecular docking analysis.

Daiana Colibăşanu, Vlad Groza, Maria Antonietta Occhiuzzi, Fedora Grande, Mihai Udrescu, Lucreția Udrescu

Abstract read
In one paragraph

Article in Frontiers in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Daiana Colibăşanu *Center for Drug Data Analysis, Cheminformatics, and the Internet of Medical Things, Victor Babeş University of Medicine and Pharmacy Timişoara, Timişoara, Romania.
Vlad Groza *Department of Computer and Information Technology, Politehnica University Timişoara, Timişoara, Romania.
Maria Antonietta Occhiuzzi *Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Italy.
Fedora GrandeDepartment of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Italy.
Mihai UdrescuDepartment of Computer and Information Technology, Politehnica University Timişoara, Timişoara, Romania.
Lucreția UdrescuCenter for Drug Data Analysis, Cheminformatics, and the Internet of Medical Things, Victor Babeş University of Medicine and Pharmacy Timişoara, Timişoara, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Drug repositioning-finding new therapeutic uses for existing drugs-can dramatically reduce development time and cost, but requires efficient computational frameworks to generate and validate repositioning hypotheses. Network-based methods can uncover drug communities with shared pharmacological properties, while molecular docking offers mechanistic insights by predicting drug-target binding. Methods: We introduce an end-to-end, fully automated pipeline that (1) constructs a tripartite drug-gene-disease network from DrugBank and DisGeNET, (2) projects it into a drug-drug similarity network for community detection, (3) labels communities Results: After filtering for connectivity and size, 12 robust communities emerged from the initial 34 clusters. The pipeline correctly matched 53.4% of drugs to their ATC level 1 community label Conclusion: Our integrated pipeline effectively integrates network-based community analysis and automated ATC labeling with literature and docking analysis, narrowing the search space for

Indexed as

ATC labelingdrug-disease networkdrug-drug similarity networkdrug repositioningmolecular docking

Identifiers

PMID41210919
PMCPMC12589059

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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.