Efficient generative modeling of protein sequences using simple autoregressive models
Deep learning is a powerful tool for the design of novel protein sequences, yet can be computationally very inefficient. Here the authors propose using simple forecasting models to efficiently generate a large number of novel protein structures.
Guardado en:
Autores principales: | , , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/26264fe401544f27b9d9bdba0ab20a68 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:26264fe401544f27b9d9bdba0ab20a68 |
---|---|
record_format |
dspace |
spelling |
oai:doaj.org-article:26264fe401544f27b9d9bdba0ab20a682021-12-02T16:57:09ZEfficient generative modeling of protein sequences using simple autoregressive models10.1038/s41467-021-25756-42041-1723https://doaj.org/article/26264fe401544f27b9d9bdba0ab20a682021-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25756-4https://doaj.org/toc/2041-1723Deep learning is a powerful tool for the design of novel protein sequences, yet can be computationally very inefficient. Here the authors propose using simple forecasting models to efficiently generate a large number of novel protein structures.Jeanne TrinquierGuido UguzzoniAndrea PagnaniFrancesco ZamponiMartin WeigtNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021) |
institution |
DOAJ |
collection |
DOAJ |
language |
EN |
topic |
Science Q |
spellingShingle |
Science Q Jeanne Trinquier Guido Uguzzoni Andrea Pagnani Francesco Zamponi Martin Weigt Efficient generative modeling of protein sequences using simple autoregressive models |
description |
Deep learning is a powerful tool for the design of novel protein sequences, yet can be computationally very inefficient. Here the authors propose using simple forecasting models to efficiently generate a large number of novel protein structures. |
format |
article |
author |
Jeanne Trinquier Guido Uguzzoni Andrea Pagnani Francesco Zamponi Martin Weigt |
author_facet |
Jeanne Trinquier Guido Uguzzoni Andrea Pagnani Francesco Zamponi Martin Weigt |
author_sort |
Jeanne Trinquier |
title |
Efficient generative modeling of protein sequences using simple autoregressive models |
title_short |
Efficient generative modeling of protein sequences using simple autoregressive models |
title_full |
Efficient generative modeling of protein sequences using simple autoregressive models |
title_fullStr |
Efficient generative modeling of protein sequences using simple autoregressive models |
title_full_unstemmed |
Efficient generative modeling of protein sequences using simple autoregressive models |
title_sort |
efficient generative modeling of protein sequences using simple autoregressive models |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/26264fe401544f27b9d9bdba0ab20a68 |
work_keys_str_mv |
AT jeannetrinquier efficientgenerativemodelingofproteinsequencesusingsimpleautoregressivemodels AT guidouguzzoni efficientgenerativemodelingofproteinsequencesusingsimpleautoregressivemodels AT andreapagnani efficientgenerativemodelingofproteinsequencesusingsimpleautoregressivemodels AT francescozamponi efficientgenerativemodelingofproteinsequencesusingsimpleautoregressivemodels AT martinweigt efficientgenerativemodelingofproteinsequencesusingsimpleautoregressivemodels |
_version_ |
1718382613536702464 |