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.
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/26264fe401544f27b9d9bdba0ab20a68 |
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| Sumario: | 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. |
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