Evidence map›Paper›PMID 36477604›Full record

ArticleScientific reports2022

DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features.

Yinyin Wang, Jehad Aldahdooh, Yingying Hu, Hongbin Yang, Markus Vähä-Koskela, Jing Tang, Ziaurrehman Tanoli

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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  14. Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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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

7 authors.

Yinyin WangResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Jehad AldahdoohResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Yingying HuResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Hongbin YangDepartment of Chemistry, University of Cambridge, Cambridge, UK.
Markus Vähä-KoskelaInstitute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
Jing TangResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. jing.tang@helsinki.fi.
Ziaurrehman TanoliInstitute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. zia.rehman@helsinki.fi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The drug development process consumes 9-12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data to repurpose drugs for 669 diseases from 22 groups, including various cancers, musculoskeletal, infections, cardiovascular, and skin diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurposed scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. At DrugRepo score ≥ 0.4, we repurposed 516 approved drugs across 545 diseases. Moreover, hundreds of novel predicted compounds can be matched with ongoing studies at clinical trials. Our analysis is supported by a web tool available at: http://drugrepo.org/ .

Indexed as

Genomics

Identifiers

PMID36477604
PMCPMC9729186

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Registered trials

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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.