AI Article Synopsis

  • The study reviews computer-aided drug design (CADD) techniques, focusing on their role in drug development and analyzing trends in research publications from 2000 to July 2023 using bibliometrics.
  • A total of 2031 publications were analyzed, with leading contributions from the U.S. and China, and MacKerell AD emerging as the most influential author, while the Journal of Medicinal Chemistry was the most frequently cited.
  • The findings highlight active global collaboration in CADD research, showcasing various technologies that enhance drug discovery, and underscore the growing impact of artificial intelligence in the field's future.

Article Abstract

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175770PMC
http://dx.doi.org/10.1097/JS9.0000000000001289DOI Listing

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