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AI can help to speed up drug discovery — but only if we give it the right data

Artificial-intelligence tools that enable companies to share data about drug candidates while keeping sensitive information safe can unleash the potential of machine learning and cutting-edge lab techniques, for the common good.
  1. Marissa Mock

    1. Marissa Mock is senior director of research at Biologic Therapeutic Discovery, Amgen, Thousand Oaks, California, USA.

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  2. Suzanne Edavettal

    1. Suzanne Edavettal is associate vice-president for research at Biologic Therapeutic Discovery, Amgen, Thousand Oaks, California, USA.

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  3. Christopher Langmead

    1. Christopher Langmead is director of data sciences at Biologic Therapeutic Discovery, Amgen, Thousand Oaks, California, USA.

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  4. Alan Russell

    1. Alan Russell is vice-president for research at Biologic Therapeutic Discovery, Amgen, Thousand Oaks, California, USA.

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Wide low-angle view of a white robotic arm on a table, surrounded by glass

Leaps in technology are supporting AI-guided drug design, such as this fully robotic workstation that can purify proteins and move liquids. Credit: Daniel Yoo

There is a troubling crunch point in the development of drugs made from proteins. Fewer than 10% of such drug candidates succeed in clinical trials1. Failure at this late stage of development costs between US$30 million and $310 million per clinical trial2, potentially costing billions of dollars per drug, and wastes years of research while patients wait for a treatment.

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Nature 621, 467-470 (2023)

doi: https://doi.org/10.1038/d41586-023-02896-9

References

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Competing Interests

All co-authors are employees and stockholders of Amgen, Inc.

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