A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023)
Drug repositioning is a technique to investigate whether a drug already approved for one disease will function for another disease not included in the original design. This process has attracted much interest in the last two decades because it is less time-consuming and more cost-efficient than trad...
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Elsevier
2025-05-01
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| author | Alejandro I. Trejo-Castro Emmanuel Martinez-Ledesma Antonio Martinez-Torteya |
| author_facet | Alejandro I. Trejo-Castro Emmanuel Martinez-Ledesma Antonio Martinez-Torteya |
| author_sort | Alejandro I. Trejo-Castro |
| collection | DOAJ |
| description | Drug repositioning is a technique to investigate whether a drug already approved for one disease will function for another disease not included in the original design. This process has attracted much interest in the last two decades because it is less time-consuming and more cost-efficient than traditional drug discovery. Its impact has been observed mainly in the pandemic setting, where the severity and lack of cure or vaccine prompted the testing of existing drugs. This bibliometric study aims to show the conceptual and intellectual structure of the progress made in this field using computational or in silico techniques. We searched in Scopus in February 2024 and yielded a final database of 5320 articles, and then we filtered, cleaned, and harmonized the data. We analyzed the data with the help of Bibliometrix, Scimago Graphica, and custom code using R to underscore the most frequent journals, authors, countries, affiliations, and keywords. To determine the most studied diseases, we grouped similar terms (e.g., Alzheimer's disease, dementia) into clusters using a custom dictionary and analyzed their frequency trends over time. We highlight that the most reported databases in terms of keywords were Connectivity Map and DrugBank. The most studied diseases were COVID-19, cancer, infectious diseases (Chagas, malaria, tuberculosis), and neurological diseases such as Alzheimer's and Parkinson's. Overall, we concluded that drug repurposing will continue to be of interest and a realistic solution for drug discovery. |
| format | Article |
| id | doaj-art-a6d2ba6231a9417aba2304833d30d5b9 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| spelling | doaj-art-a6d2ba6231a9417aba2304833d30d5b92025-08-20T03:07:25ZengElsevierHeliyon2405-84402025-05-011110e4275010.1016/j.heliyon.2025.e42750A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023)Alejandro I. Trejo-Castro0Emmanuel Martinez-Ledesma1Antonio Martinez-Torteya2Tecnológico de Monterrey, School of Medicine and Health Sciences, 64710, Monterrey, Nuevo León, Mexico; Universidad de Monterrey, School of Engineering and Technology, 66238, San Pedro Garza García, Nuevo León, MexicoTecnológico de Monterrey, School of Medicine and Health Sciences, 64710, Monterrey, Nuevo León, Mexico; Tecnológico de Monterrey, Institute for Obesity Research, 64710, Monterrey, Nuevo León, Mexico; Corresponding author. Tecnológico de Monterrey, Institute for Obesity Research, 64710, Monterrey, Nuevo León, Mexico.Universidad de Monterrey, School of Engineering and Technology, 66238, San Pedro Garza García, Nuevo León, Mexico; Corresponding author.Drug repositioning is a technique to investigate whether a drug already approved for one disease will function for another disease not included in the original design. This process has attracted much interest in the last two decades because it is less time-consuming and more cost-efficient than traditional drug discovery. Its impact has been observed mainly in the pandemic setting, where the severity and lack of cure or vaccine prompted the testing of existing drugs. This bibliometric study aims to show the conceptual and intellectual structure of the progress made in this field using computational or in silico techniques. We searched in Scopus in February 2024 and yielded a final database of 5320 articles, and then we filtered, cleaned, and harmonized the data. We analyzed the data with the help of Bibliometrix, Scimago Graphica, and custom code using R to underscore the most frequent journals, authors, countries, affiliations, and keywords. To determine the most studied diseases, we grouped similar terms (e.g., Alzheimer's disease, dementia) into clusters using a custom dictionary and analyzed their frequency trends over time. We highlight that the most reported databases in terms of keywords were Connectivity Map and DrugBank. The most studied diseases were COVID-19, cancer, infectious diseases (Chagas, malaria, tuberculosis), and neurological diseases such as Alzheimer's and Parkinson's. Overall, we concluded that drug repurposing will continue to be of interest and a realistic solution for drug discovery.http://www.sciencedirect.com/science/article/pii/S2405844025011314BioinformaticsComputational biologyDeep learningDrug repurposingIn silicoMachine learning |
| spellingShingle | Alejandro I. Trejo-Castro Emmanuel Martinez-Ledesma Antonio Martinez-Torteya A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) Heliyon Bioinformatics Computational biology Deep learning Drug repurposing In silico Machine learning |
| title | A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) |
| title_full | A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) |
| title_fullStr | A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) |
| title_full_unstemmed | A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) |
| title_short | A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) |
| title_sort | bibliometric review on in silico drug repurposing performance analysis science mapping and text mining 2000 2023 |
| topic | Bioinformatics Computational biology Deep learning Drug repurposing In silico Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025011314 |
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