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Noe Frank
Noe Frank
Microsoft Research
Bestätigte E-Mail-Adresse bei fu-berlin.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Markov models of molecular kinetics: Generation and validation
JH Prinz, H Wu, M Sarich, B Keller, M Senne, M Held, JD Chodera, ...
The Journal of chemical physics 134, 174105, 2011
11182011
Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation
M Wieczorek, ET Abualrous, J Sticht, M Álvaro-Benito, S Stolzenberg, ...
Frontiers in immunology 8, 292, 2017
8492017
Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations
F Noé, C Schütte, E Vanden-Eijnden, L Reich, TR Weikl
Proceedings of the National Academy of Sciences 106 (45), 19011, 2009
8112009
Identification of slow molecular order parameters for Markov model construction
G Pérez-Hernández, F Paul, T Giorgino, G De Fabritiis, F Noé
The Journal of chemical physics 139 (1), 07B604_1, 2013
8052013
PyEMMA 2: A software package for estimation, validation, and analysis of Markov models
MK Scherer, B Trendelkamp-Schroer, F Paul, G Pérez-Hernández, ...
Journal of chemical theory and computation 11 (11), 5525-5542, 2015
7982015
Markov state models of biomolecular conformational dynamics
JD Chodera, F Noé
Current opinion in structural biology 25, 135-144, 2014
6622014
An introduction to Markov state models and their application to long timescale molecular simulation
GR Bowman, VS Pande, F Noé
Springer Science & Business Media, 2013
5342013
Transition networks for modeling the kinetics of conformational change in macromolecules
F Noé, S Fischer
Current opinion in structural biology 18 (2), 154-162, 2008
4832008
VAMPnets for deep learning of molecular kinetics
A Mardt, L Pasquali, H Wu, F Noé
Nature communications 9 (1), 5, 2018
4672018
Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states
F Noé, I Horenko, C Schütte, JC Smith
The Journal of chemical physics 126, 155102, 2007
4512007
Machine learning for molecular simulation
F Noé, A Tkatchenko, KR Müller, C Clementi
Annual review of physical chemistry 71, 361-390, 2020
4492020
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
F Noé, S Olsson, J Köhler, H Wu
Science 365 (6457), eaaw1147, 2019
4392019
Spatiotemporal control of endocytosis by phosphatidylinositol-3, 4-bisphosphate
Y Posor, M Eichhorn-Gruenig, D Puchkov, J Schöneberg, A Ullrich, ...
Nature 499 (7457), 233-237, 2013
4262013
Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models
N Plattner, F Noé
Nature communications 6 (1), 7653, 2015
3652015
Deep-neural-network solution of the electronic Schrödinger equation
J Hermann, Z Schätzle, F Noé
Nature Chemistry 12 (10), 891-897, 2020
3502020
Crystal structure of nucleotide-free dynamin
K Faelber, Y Posor, S Gao, M Held, Y Roske, D Schulze, V Haucke, F Noé, ...
Nature, 2011
3392011
HTMD: high-throughput molecular dynamics for molecular discovery
S Doerr, MJ Harvey, F Noé, G De Fabritiis
Journal of chemical theory and computation 12 (4), 1845-1852, 2016
3312016
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
R Winter, F Montanari, F Noé, DA Clevert
Chemical science 10 (6), 1692-1701, 2019
3242019
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
C Wehmeyer, F Noé
The Journal of chemical physics 148 (24), 241703, 2018
3222018
Machine learning of coarse-grained molecular dynamics force fields
J Wang, S Olsson, C Wehmeyer, A Pérez, NE Charron, G De Fabritiis, ...
ACS central science 5 (5), 755-767, 2019
3192019
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