Collection 

Machine learning in protein science

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Open
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Over the past few years, deep-learning-based methods have revolutionized the field of protein structure prediction. Tools like AlphaFold can now reliably model the structure of a protein based solely on its amino acid sequence, even when few homologous sequences and structures are available. These advances promise to transform our understanding of individual proteins’ biological functions, with implications for drug discovery, de novo protein design, and protein-protein interaction research.

This Collection will feature Articles applying this exciting new technology in these and other areas.

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Illustration of protein

Editors

Collections articles undergo Scientific Reports' standard peer review process and are subject to all of the journal’s standard policies. This includes the journal’s policy on competing interests. The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.

This Collection has not been supported by sponsorship.