Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

医学 文献计量学 计算机辅助 计算机科学 药效团 管理科学 数据科学 图书馆学 生物信息学 工程类 生物 程序设计语言
作者
Zhenhui Wu,Shupeng Chen,Yihao Wang,Fangyang Li,Huanhua Xu,Maoxing Li,Yingjian Zeng,Zhenfeng Wu,Yue Gao
出处
期刊:International Journal of Surgery [Wolters Kluwer]
卷期号:110 (6): 3848-3878 被引量:67
标识
DOI:10.1097/js9.0000000000001289
摘要

AIM: Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. METHODS: A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace. RESULTS: A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. CONCLUSIONS: Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.
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