Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov 30;15(12):1492.
doi: 10.3390/ph15121492.

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Erik Karger  1 , Marko Kureljusic  2
Affiliations

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Erik Karger et al. Pharmaceuticals (Basel). .

Abstract

Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.

Keywords: artificial intelligence; bibliometric study; deep learning; drug development; drug discovery; machine learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of the publications among disciplines.
Figure 2
Figure 2
Number of publications per year.
Figure 3
Figure 3
Word clouds of the most frequently appearing author keywords: (a) years 1991–2007 (316 articles) and (b) years 2008–2013 (471 articles).
Figure 4
Figure 4
Word clouds of the most frequently appearing author keywords: (a) years 2015–2019 (1197 articles) and (b) years 2020–2022 (1789 articles).
Figure 5
Figure 5
Keyword co-occurrence of the most-used author and indexed keywords.
Figure 6
Figure 6
Overview of the data collection and the exclusion of articles.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Poduri R. Historical Perspective of Drug Discovery and Development. In: Poduri R., editor. Drug Discovery and Development. Springer; Singapore: 2021. pp. 1–10.
    1. Roser M., Ortiz-Ospina E., Ritchie H. Life Expectancy. [(accessed on 29 October 2022)]. Available online: https://ourworldindata.org/life-expectancy.
    1. Zanders E.D. The Science and Business of Drug Discovery: Demystifying the Jargon. 2nd ed. Springer; Cham, Switzerland: 2020.
    1. Weinstein D.B., France D.S. Jumping into the 20th century before it is too late: Is laboratory robotics still in its infancy? J. Automat. Chem. 1992;14:59–63. doi: 10.1155/S1463924692000142. - DOI - PMC - PubMed
    1. Coates W.J., Hunter D.J., MacLachlan W.S. Successful implementation of automation in medicinal chemistry. Drug Discov. Today. 2000;5:521–527. doi: 10.1016/S1359-6446(00)01571-3. - DOI - PubMed

LinkOut - more resources