A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) - Alejandro I. Trejo-Castro; Emmanuel Martinez-Ledesma; Antonio Martinez-Torteya (Heliyon)
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.