The confluence of machine learning and multiscale simulations.
Curr Opin Struct Biol
; 80: 102569, 2023 06.
Article
en En
| MEDLINE
| ID: mdl-36966691
ABSTRACT
Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Simulación de Dinámica Molecular
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Curr Opin Struct Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2023
Tipo del documento:
Article