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AI for Drug Repurposing in the Pandemic Response

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Artificial Intelligence in Covid-19

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Abstract

Given the time criticality of finding treatments for the novel COVID-19 pandemic disease, drug repurposing has proved to be a vital strategy as the first response while de novo drug and vaccine developments are underway. Furthermore, Artificial Intelligence (AI) has also accelerated drug development in general. Key desirable features of AI that support a rapid and sustained response along the pandemic timeline include technical flexibility and efficiency (i.e. speed, resource-efficiency, algorithm adaptability), and clinical applicability and acceptability (i.e. scientific rigor, physiological applicability and practical implementation of proposed drugs). This chapter reviews a selection of AI-based applications used in drug development targeting COVID-19, including IDentif.AI—a small data platform for a rapid identification of optimal drug combinations, to illustrate the potential of AI in drug repurposing. The benefits and limitations of using Real-World Data are also discussed. The response to the COVID-19 pandemic has offered multiple learnings which highlight the need to strengthen both short- and long-term strategies in developing AI technologies, scientific and regulatory frameworks as well as worldwide collaborations to enable effective preparedness for future epidemic and pandemic risks.

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Authors and Affiliations

  1. The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore

    Anh T. L. Truong, Agata Blasiak, Mathias Egermark & Dean Ho

  2. Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore

    Anh T. L. Truong, Agata Blasiak & Dean Ho

  3. The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Agata Blasiak & Dean Ho

  4. Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Dean Ho

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Correspondence to Mathias Egermark .

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  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Weizmann Institute of Science, Rehovot, Israel

    Yonina C. Eldar

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Truong, A.T.L., Blasiak, A., Egermark, M., Ho, D. (2022). AI for Drug Repurposing in the Pandemic Response. In: Lidströmer, N., Eldar, Y.C. (eds) Artificial Intelligence in Covid-19. Springer, Cham. https://doi.org/10.1007/978-3-031-08506-2_3

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