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Review
. 2023 Jul 14;381(6654):164-170.
doi: 10.1126/science.adh1114. Epub 2023 Jul 13.

Leveraging artificial intelligence in the fight against infectious diseases

Felix Wong  1   2 , Cesar de la Fuente-Nunez  3   4   5 , James J Collins  1   2   6
Affiliations
Review

Leveraging artificial intelligence in the fight against infectious diseases

Felix Wong et al. Science. .

Abstract

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

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Conflict of interest statement

Competing interests: J.J.C. is scientific co-founder and scientific advisory board chair of EnBiotix, an antibiotic drug discovery company, and Phare Bio, a non-profit venture focused on antibiotic drug development. C.F.N. provides consulting services to Invaio Sciences and is a member of the scientific advisory boards of Nowture S.L. and Phare Bio. F.W. declares no competing interests.

Figures

Fig. 1.
Fig. 1.. Artificial intelligence can predict anti-infective drug activity, drug-target interactions, and therapeutic design.
Examples of AI model inputs, model architectures or types, and model outputs relevant to anti-infective drug discovery include those focusing on drug activity (A), drug-target interactions and mechanisms of action (B), and programmable therapeutic design (C). Inputs, models, and outputs shown are representative, in part, of those discussed in refs (–,–,–33,36,39,40).
Fig. 2.
Fig. 2.. Artificial intelligence can elucidate infection biology, facilitate vaccine design, and inform treatment strategies.
Examples of AI model inputs, model architectures or types, and model outputs focusing on infection biology (A), vaccine design (B), and anti-infective drug treatment strategies (C). Inputs, models, and outputs shown are representative, in part, of those discussed in refs (–,–54).
Fig. 3.
Fig. 3.. Artificial intelligence can facilitate synthetic biology research and diagnostics development.
Examples of AI model inputs, model architectures or types, and model outputs relevant to the development of synthetic biology-based diagnostics (A) and the development of other forms of diagnostics, including those based on sequencing, mass spectrometry, and imaging (B). Inputs, models, and outputs shown are representative, in part, of those discussed in refs (–,–74).
See this image and copyright information in PMC

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