VAMPnets for deep learning of molecular kinetics
Extracting kinetic models from high-throughput molecular dynamics (MD) simulations is laborious and prone to human error. Here the authors introduce a deep learning framework that automates construction of Markov state models from MD simulation data.
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2018
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/8a30e6ffd9b7407e84418e912a435af9 |
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| Sumario: | Extracting kinetic models from high-throughput molecular dynamics (MD) simulations is laborious and prone to human error. Here the authors introduce a deep learning framework that automates construction of Markov state models from MD simulation data. |
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