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GraphVAMPNet, using graph neural networks and variational approach to
  markov processes for dynamical modeling of biomolecules

GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules

12 January 2022
Mahdi Ghorbani
Samarjeet Prasad
Jeffery B. Klauda
B. Brooks
    GNN
ArXivPDFHTML

Papers citing "GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules"

3 / 3 papers shown
Title
Coarse Graining Molecular Dynamics with Graph Neural Networks
Coarse Graining Molecular Dynamics with Graph Neural Networks
B. Husic
N. Charron
Dominik Lemm
Jiang Wang
Adria Pérez
...
Yaoyi Chen
Simon Olsson
Gianni de Fabritiis
Frank Noé
C. Clementi
AI4CE
29
156
0
22 Jul 2020
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
231
3,202
0
24 Nov 2016
Variational Koopman models: slow collective variables and molecular
  kinetics from short off-equilibrium simulations
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Hao Wu
Feliks Nuske
Fabian Paul
Stefan Klus
P. Koltai
Frank Noé
99
126
0
20 Oct 2016
1