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Grant support

This work was supported by the European Project: Repo4EU https://repo4.eu/Grant no. 101057619 within the Horizon Europe Research and Innovation Programme. We thank specially Dr. Emre Guney from STALICLA Discovery and Data Science Unit for his support in providing articles and resources that were used in this review.

Analysis of institutional authors

Perdomo-Quinteiro, PabloCorresponding AuthorBelmonte-Hernandez, AlbertoAuthor

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Review

Knowledge Graphs for drug repurposing: a review of databases and methods

Publicated to:Briefings In Bioinformatics. 25 (6): bbae461- - 2024-09-26 25(6), DOI: 10.1093/bib/bbae461

Authors: Perdomo-Quinteiro, Pablo; Belmonte-Hernandez, Alberto

Affiliations

Univ Politecn Madrid, Grp Aplicac Telecomunicac Visuales, Ave Complutense 30, Madrid 28040, Spain - Author

Abstract

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

Keywords

AlgorithmsArtificial intelligenceComputational biologyDatabases, factualDrug repositioningDrug repurposingExplainabilitExplainabilityGraph networksHumansKnowledge graphs

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Briefings In Bioinformatics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 4/66, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mathematical & Computational Biology. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-06-30:

  • Scopus: 3

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-30:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 35.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 35 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.85.
  • The number of mentions on the social network X (formerly Twitter): 3 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PERDOMO QUINTEIRO, PABLO) and Last Author (BELMONTE HERNÁNDEZ, ALBERTO).

the author responsible for correspondence tasks has been PERDOMO QUINTEIRO, PABLO.