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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.
Zhao, Jinlong (57204615579); Zeng, Lingfeng (55635553800); Wei, Wanjia (59453408000); Liang, Guihong (57194134199); Yang, Weiyi (56047615500); Fu, Haoyang (56048255500); Zeng, Yuping (59422387300); Liu, Jun (55351349000); Zhao, Shuai (57205881934)
Knowledge Graph of Endoscopic Techniques Applied to the Treatment of Lumbar Disc Herniation: A Bibliometric Analysis
2024
10.1097/BSD.0000000000001648
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210777192&doi=10.1097%2fBSD.0000000000001648&partnerID=40&md5=6350efbc348cb2d2c3f6a29f0268e690
The Second Clinical College, Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, China; The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, China; The Research Team on Bone and Joint Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, China; Medical College of Acupuncture-Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, China; Guangdong Second Chinese Medicine Hospital, Guangdong Province Engineering Technology, Research Institute of Traditional Chinese Medicine, Guangzhou, China
All Open Access; Green Open Access
Scopus
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