Rapid progress in molecular and cell biology is enabling a deeper understanding of the mechanisms by which pathogens infect human cells. A crucial step forward has been the development of AI tools such as AlphaFold, which predicts the 3D structure of a protein based on its amino-acid sequence. Such tools can characterise key molecules (including their mechanisms and functions) faster than traditional experimental methods. Likewise, ongoing improvements in the speed and accuracy of DNA sequencing mean there is now a wealth of data on the genomics of pathogenic and non-pathogenic microorganisms.

Future Horizons:

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5-yearhorizon

Infection scenarios can be reliably predicted

Reliable predictions of which pathogens will be able to infect which hosts, based on genotype, become possible. These will be underpinned by systematic mutagenesis experiments. Researchers gain improved understanding of the holistic impacts of infections, such as viruses causing cancer, and chronic conditions like Long COVID.

10-yearhorizon

Pathogen genotypes reveal likely consequences of infection

A pathogen’s phenotype, including the type of illness it causes, can be reliably predicted from its genotype. AI is in routine use for development of drugs and vaccines.

25-yearhorizon

AI models eliminate animal research

Researchers achieve the elimination of most animal research on pathogens and of most in vitro experiments. Instead, AI models trained on datasets like the Human Cell Atlas provide insights. Gain-of-function research is also performed computationally in most cases. Targeted treatments arise for conditions like multiple sclerosis that are linked to chronic infections from viruses such as Epstein-Barr.

Other work is improving our understanding of how the immune system interacts with other bodily systems in intricate ways. A seemingly simple infection might set off a complex cascade of changes within the body, for example, especially in conditions like long COVID. Conversely, children’s plasma proteome is remodelled by exposure to malaria, leading to natural immunity, and understanding this could lead to new therapeutics.⁵

Bringing such distinct datasets together is challenging but offers the potential for great insights.⁶ It has become possible to predict COVID-19 mortality solely by analysing gene expression in the patient’s blood, for example.⁷ Similarly, rich biological datasets are helping to explain long-established but mysterious patterns in infection biology. For instance, specific changes in the immune system may account for the much higher death rates from COVID-19 among the elderly.⁸

AI is mining insights from the vast datasets generated by research. For example, machine-learning models can predict how virus proteins will interact with host proteins,⁹ and forecast long-COVID outcomes based on immunological data.¹⁰ However, AI expertise and infrastructure is currently centred in the rich West: there is an urgent need to expand AI access in low and middle income countries, where it can enable biomedical progress even in low-resource settings.

Decoding infectivity - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.