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Dual use of artificial-intelligence-powered drug discovery

Nature Machine Intelligence volume 4, pages 189–191 (2022)Cite this article

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An international security conference explored how artificial intelligence (AI) technologies for drug discovery could be misused for de novo design of biochemical weapons. A thought experiment evolved into a computational proof.

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Fig. 1: A t-SNE plot visualization of the LD50 dataset and top 2,000 MegaSyn AI-generated and predicted toxic molecules illustrating VX.

References

  1. Spiez Convergence Conference https://www.spiezlab.admin.ch/en/home/meta/refconvergence.html (2021).

  2. Urbina, F., Lowden, C. T., Culberson, J. C. & Ekins, S. https://doi.org/10.33774/chemrxiv-2021-nlwvs (2021).

  3. Mansouri, K. et al. Environ. Health Perspect. 129, 047013 (2021).

    Article  Google Scholar 

  4. Blaschke, T. et al. J. Chem. Inf. Model. 60, 5918–5922 (2020).

    Article  Google Scholar 

  5. National Research Council Committee on Toxicology. https://www.ncbi.nlm.nih.gov/books/NBK233724/ (National Academies Press, 1997).

  6. Aroniadou-Anderjaska, V., Apland, J. P., Figueiredo, T. H., De Araujo Furtado, M. & Braga, M. F. Neuropharmacology 181, 108298 (2020).

    Article  Google Scholar 

  7. Genheden, S. et al. J. Cheminform. 12, 70 (2020).

    Article  Google Scholar 

  8. Coley, C. W. et al. Science 365, eaax1566 (2019).

    Article  Google Scholar 

  9. Dix, D. J. et al. Toxicol. Sci. 95, 5–12 (2007).

    Article  Google Scholar 

  10. Hutson, M. The New Yorker https://www.newyorker.com/tech/annals-of-technology/who-should-stop-unethical-ai (2021).

  11. Brown, T. B. et al. Preprint at arXiv https://arxiv.org/abs/2005.14165 (2020).

  12. Prunkl, C. E. A. et al. Nat. Mach. Intell. 3, 104–110 (2021).

    Article  Google Scholar 

  13. Organisation for the Prohibition of Chemical Weapons. The Hague Ethical Guidelines https://www.opcw.org/hague-ethical-guidelines (2021).

Download references

Acknowledgements

We are grateful to the organizers and participants of the Spiez Convergence conference 2021 for their feedback and questions. C.I. contributed to this article in his personal capacity. The views expressed in this article are those of the authors only and do not necessarily represent the position or opinion of Spiez Laboratory or the Swiss Government. We kindly acknowledge US National Institutes of Health funding under grant R44GM122196-02A1 from the National Institute of General Medical Sciences and 1R43ES031038-01 and 1R43ES033855-01 from the National Institute of Environmental Health Sciences for our machine learning software development and applications. Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under grants R43ES031038 and 1R43ES033855-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA

    Fabio Urbina & Sean Ekins

  2. Department of War Studies and Department of Global Health & Social Medicine, King’s College London, London, UK

    Filippa Lentzos

  3. Spiez Laboratory, Federal Department of Defence, Civil Protection and Sports, Spiez, Switzerland

    Cédric Invernizzi

Authors
  1. Fabio Urbina
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  2. Filippa Lentzos
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  3. Cédric Invernizzi
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  4. Sean Ekins
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Correspondence to Sean Ekins.

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Competing interests

F.U. and S.E. work for Collaborations Pharmaceuticals, Inc. F.L. and C.I. have no conflicts of interest.

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Nature Machine Intelligence thanks Gisbert Schneider and Carina Prunkl for their contribution to the peer review of this work.

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Urbina, F., Lentzos, F., Invernizzi, C. et al. Dual use of artificial-intelligence-powered drug discovery. Nat Mach Intell 4, 189–191 (2022). https://doi.org/10.1038/s42256-022-00465-9

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