AlphaFold and the future of structural biology

This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

Detalles Bibliográficos
Autores principales: Read, Randy J., Baker, Edward N., Bond, Charles S., Garman, Elspeth F., van Raaij, Mark J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
Materias:
Editorial
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10324484/
https://www.ncbi.nlm.nih.gov/pubmed/37358477
http://dx.doi.org/10.1107/S2052252523004943
Descripción
Sumario:This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

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