Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation.
J Chem Phys
; 149(21): 216101, 2018 Dec 07.
Article
em En
| MEDLINE
| ID: mdl-30525733
ABSTRACT
As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the time scale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according to the variational approach to conformational dynamics. We provide evidence that the VDE framework [Hernández et al., Phys. Rev. E 97, 062412 (2018)], which uses this autocorrelation loss along with a time-lagged reconstruction loss, obtains a variationally optimized latent coordinate in comparison with related loss functions. We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article