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
Review
. 2021 Dec 21;5(6):803-813.
doi: 10.1042/ETLS20210223.

Artificial intelligence unifies knowledge and actions in drug repositioning

Zheng Yin  1 , Stephen T C Wong  1
Affiliations
Review

Artificial intelligence unifies knowledge and actions in drug repositioning

Zheng Yin et al. Emerg Top Life Sci. .

Abstract

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

Keywords: artificial intelligence; computational biology; deep learning; drug repositioning; systems medicine.

PubMed Disclaimer

Conflict of interest statement

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1.
Figure 1.. The conceptual workflows for major types of drug repositioning methods.
Each type of methods features unique combinations of disease (light yellow boxes) and drug (light blue) related data and knowledge, computational modeling methods (light green), and rigorous testing and validation.
Figure 2.
Figure 2.. An iterative loop of prediction→validation→modeling for improving the success rate of candidate predictions.
This iterative workflow converges disease (yellow box) and drug (blue box) related data and knowledge and uses computational modeling (light green) guided validation to compensate for the concerns of low predictive accuracy caused by factors such as imperfect drug effect profiles.
See this image and copyright information in PMC

References

    1. Fjelland R (2020) Why general artificial intelligence will not be realized. Humanit. Soc. Sci. Commun 7, 10 10.1057/S41599-020-0494-4 - DOI
    1. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 10.1038/nature16961 - DOI - PubMed
    1. Schrittwieser J, Antonoglou I, Hubert T, Simonyan K, Sifre L, Schmitt S et al. (2020) Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588, 604–609 10.1038/s41586-020-03051-4 - DOI - PubMed
    1. Toosi A, Bottino AG, Saboury B, Siegel E and Rahmim A (2021) A brief history of AI: how to prevent another winter (a critical review). PET Clin. 16, 449–469 https://doi.org/10.1016Zi.cpet.2021.07.001 - PubMed
    1. Haenlein M and Kaplan A (2019) A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manage Rev 61, 5–14 10.1177/0008125619864925 - DOI

Publication types

MeSH terms

LinkOut - more resources