Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations
Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. This can be accomplished by sampling protein conformational space with molecular simulation methodologies. De...
Guardado en:
Autores principales: | Venkata K. Ramaswamy, Samuel C. Musson, Chris G. Willcocks, Matteo T. Degiacomi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
American Physical Society
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/82f7acafe19041bc9e2a053f42497bd8 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
An Effective Bicubic Convolution Interpolation-Based Iterative Luma Optimization for Enhancing Quality in Chroma Subsampling
por: Kuo-Liang Chung, et al.
Publicado: (2021) -
A Novel Lidar Signal Denoising Method Based on Convolutional Autoencoding Deep Learning Neural Network
por: Minghuan Hu, et al.
Publicado: (2021) -
The cubic B-spline interpolation method for numerical point solutions of conformable boundary value problems
por: Soumia Tayebi, et al.
Publicado: (2022) -
The extremal function of interpolation formulas in W2(2,0) space
por: Boltaev, A.K., et al.
Publicado: (2021) -
Explore Protein Conformational Space With Variational Autoencoder
por: Hao Tian, et al.
Publicado: (2021)