Artículo

Trends in research on AI-aided drug discovery from 2009 to 2023: A 15-year bibliometric analysis

Resumen

Purpose: In recent years, the rapid advancement of artificial intelligence technology has brought opportunities for the acceleration and improvement of the drug discovery process by aiding in all stages of drug discovery like drug target identification and validation, virtual screening, de novo drug design, and ADMET property prediction. The present study aims to provide an overview of the developing tendency, cooperation, and influence of academic groups and individuals, hotspots, and crucial problems in the field of AI-aided drug discovery using bibliometric methods. Methods: Publications on AI-aided drug discovery published from January 1, 2009, to December 31, 2023, were retrieved from the Web of Science core collection. The document type was limited to articles or reviews, and the language was set to English. Citespace was used to conduct the bibliometric analysis. Results: A total of 9700 publications were included, and the number of them generally increased over time, with a rapid increase tendency since 2018. The US and China were the leading countries in this field. The Chinese Academy of Sciences was the most influential institution. Ekins, Sean was the most productive author and Hou, Tingjun formed the largest cooperation network. Networks and clusters of keywords highlighted terms like “virtual screening”, “expression” and “drug delivery” as focused topics, and burst analysis showed that “support vector machines”, and “classification” received the longest attention. Meanwhile the keywords “sars cov 2”, “molecular design” and “clinical trials” were hotspots in recent years. The content analysis of the co-cited literature identified the significant questions to be tackled in future research. Conclusions: This study offers a comprehensive landscape of the global contributions given to this increasingly important and prolific field of research and points out several areas that might be addressed by future research to better develop the field of AI-aided drug discovery.
Shao, Belinda (56060106500); Harlyjoy, Alphadenti (58782040400); Kozel, Olivia A. (58531580800); Still, Megan EH. (59477435700); Widodo, Setyo BP. (59476662600); Agwu, Chibueze (58700619400); Sagaityte, Emilija (57211642482); Schroeder, Christian (58556938000); Gilder, Hannah E. (57119562100); Hamzah, Radzi (57969105500); Sun, Felicia W. (57204935020); Feler, Joshua R. (57208162552); Santos, Santos (59477435800); Sawyer, Kelsey (57215428898); Svokos, Konstantina A. (8837287300); Klinge, Petra M. (6701803828); Johnson, Walter (57149404900); Baticulon, Ronnie E. (57200079877); Park, Kee B. (56680381700)
Bibliometric Analysis of Myelomeningocele Management: National Disease Burden versus Publication Volume
2025
10.1016/j.wneu.2024.11.027
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212318358&doi=10.1016%2fj.wneu.2024.11.027&partnerID=40&md5=6914669b91034443d6929a7b95affae6
Department of Neurosurgery, Warren Alpert Medical School of Brown University, Providence, RI, United States; Department of Neurosurgery, Universitas Indonesia Hospital, Depok, Indonesia; Department of Neurosurgery, University of Florida, Gainesville, FL, United States; Department of Neurosurgery, Regional General Hospital Prof. Dr. Margono Soekarjo Purwokerto, Central Java, Indonesia; Department of Neurosurgery, University of Chicago, Pritzker School of Medicine, Chicago, IL, United States; Department of Neurological Surgery, Mayo Clinic, Rochester, MN, United States; Program in Global Surgery and Social Change, Harvard University, Boston, MA, United States; Health and Biomedical Library Services, Brown University, Providence, RI, United States; Department of Neurosurgery, Loma Linda University, Loma Linda, CA, United States; Division of Neurosurgery, Department of Neurosciences, Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
All Open Access; Hybrid Gold Open Access
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