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Self-assembling peptides

AI in biomaterials discovery: generating self-assembling peptides with resource-efficient deep learning

Nature Machine Intelligence volume 6, pages 1429–1430 (2024)Cite this article

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Recurrent neural networks are efficient and capable agents for discovering new peptides with strong self-organizing capabilities.

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Fig. 1: Using hybrid RNN models to discover new self-assembling peptides.

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  • 12 December 2024

    In the version of the article initially published, Marko Njirjak’s surname was misspelled throughout the article and in ref. 1 (as Njirnak) and has now been amended in the HTML and PDF versions of the article.

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Authors and Affiliations

  1. Machine Biology Group, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  2. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  3. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  4. Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  5. Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  6. Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  7. Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  8. Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

  9. Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA

    Tianang Leng & Cesar de la Fuente-Nunez

Authors
  1. Tianang Leng
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  2. Cesar de la Fuente-Nunez
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Correspondence to Cesar de la Fuente-Nunez.

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Competing interests

C.d.l.F.-N. provides consulting services to Invaio Sciences and is a member of the scientific advisory boards of Nowture S.L., Peptidus, European Biotech Venture Builder and Phare Bio, as well as the advisory board of the Peptide Drug Hunting Consortium (PDHC).

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Leng, T., de la Fuente-Nunez, C. AI in biomaterials discovery: generating self-assembling peptides with resource-efficient deep learning. Nat Mach Intell 6, 1429–1430 (2024). https://doi.org/10.1038/s42256-024-00936-1

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