Scalable Kinetic Models: From Molecular Dynamics to Cellular Signaling

Deep-neural-network solution of the electronic Schrödinger equation

Autores: Jan Hermann, Zeno Schätzle, Frank Noé
Publicado en: Nature Chemistry, Edición 12/10, 2020, Página(s) 891-897, ISSN 1755-4330
Editor: Nature Publishing Group
DOI: 10.1038/s41557-020-0544-y

TorchMD: A Deep Learning Framework for Molecular Simulations

Autores: Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, Gianni De Fabritiis
Publicado en: Journal of Chemical Theory and Computation, Edición 17/4, 2021, Página(s) 2355-2363, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.0c01343

Machine learning for protein folding and dynamics

Autores: Frank Noé, Gianni De Fabritiis, Cecilia Clementi
Publicado en: Current Opinion in Structural Biology, Edición 60, 2020, Página(s) 77-84, ISSN 0959-440X
Editor: Elsevier BV
DOI: 10.1016/j.sbi.2019.12.005

Convergence to the fixed-node limit in deep variational Monte Carlo

Autores: Z. Schätzle, J. Hermann, F. Noé
Publicado en: The Journal of Chemical Physics, Edición 154/12, 2021, Página(s) 124108, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/5.0032836

Coarse graining molecular dynamics with graph neural networks

Autores: Brooke E. Husic, Nicholas E. Charron, Dominik Lemm, Jiang Wang, Adrià Pérez, Maciej Majewski, Andreas Krämer, Yaoyi Chen, Simon Olsson, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
Publicado en: The Journal of Chemical Physics, Edición 153/19, 2020, Página(s) 194101, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/5.0026133

Discovery of a hidden transient state in all bromodomain families

Autores: Lluís Raich, Katharina Meier, Judith Günther, Clara D. Christ, Frank Noé, Simon Olsson
Publicado en: Proceedings of the National Academy of Sciences, Edición 118/4, 2021, Página(s) e2017427118, ISSN 0027-8424
Editor: National Academy of Sciences
DOI: 10.1073/pnas.2017427118

Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1

Autores: Katja Faelber, Lea Dietrich, Jeffrey K. Noel, Florian Wollweber, Anna-Katharina Pfitzner, Alexander Mühleip, Ricardo Sánchez, Misha Kudryashev, Nicolas Chiaruttini, Hauke Lilie, Jeanette Schlegel, Eva Rosenbaum, Manuel Hessenberger, Claudia Matthaeus, Séverine Kunz, Alexander von der Malsburg, Frank Noé, Aurélien Roux, Martin van der Laan, Werner Kühlbrandt, Oliver Daumke
Publicado en: Nature, Edición 571/7765, 2019, Página(s) 429-433, ISSN 0028-0836
Editor: Nature Publishing Group
DOI: 10.1038/s41586-019-1372-3

What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models

Autores: Ernesto Suárez, Rafal P. Wiewiora, Chris Wehmeyer, Frank Noé, John D. Chodera, Daniel M. Zuckerman
Publicado en: Journal of Chemical Theory and Computation, Edición 17/5, 2021, Página(s) 3119-3133, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.0c01154

Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

Autores: Robin Winter, Floriane Montanari, Frank Noé, Djork-Arné Clevert
Publicado en: Chemical Science, Edición 10/6, 2019, Página(s) 1692-1701, ISSN 2041-6520
Editor: Royal Society of Chemistry
DOI: 10.1039/c8sc04175j

Machine Learning for Molecular Simulation

Autores: Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Publicado en: Annual Review of Physical Chemistry, Edición 71/1, 2020, Página(s) 361-390, ISSN 0066-426X
Editor: Annual Reviews, Inc.
DOI: 10.1146/annurev-physchem-042018-052331

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Autores: Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
Publicado en: ACS Central Science, 2019, ISSN 2374-7943
Editor: American Chemical Society
DOI: 10.1021/acscentsci.8b00913

Dynamic graphical models of molecular kinetics

Autores: Simon Olsson, Frank Noé
Publicado en: Proceedings of the National Academy of Sciences, Edición 116/30, 2019, Página(s) 15001-15006, ISSN 0027-8424
Editor: National Academy of Sciences
DOI: 10.1073/pnas.1901692116

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Autores: Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu
Publicado en: Science, Edición 365/6457, 2019, Página(s) eaaw1147, ISSN 0036-8075
Editor: American Association for the Advancement of Science
DOI: 10.1126/science.aaw1147

Deflation reveals dynamical structure in nondominant reaction coordinates

Autores: Brooke E. Husic, Frank Noé
Publicado en: The Journal of Chemical Physics, Edición 151/5, 2019, Página(s) 054103, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5099194

Variational selection of features for molecular kinetics

Autores: Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé
Publicado en: The Journal of Chemical Physics, Edición 150/19, 2019, Página(s) 194108, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5083040

Identification of kinetic order parameters for non-equilibrium dynamics

Autores: Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot, Frank Noé
Publicado en: The Journal of Chemical Physics, Edición 150/16, 2019, Página(s) 164120, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5083627

Targeted Adversarial Learning Optimized Sampling

Autores: Jun Zhang, Yi Isaac Yang, Frank Noé
Publicado en: The Journal of Physical Chemistry Letters, Edición 10/19, 2019, Página(s) 5791-5797, ISSN 1948-7185
Editor: American Chemical Society
DOI: 10.1021/acs.jpclett.9b02173

Kernel methods for detecting coherent structures in dynamical data

Autores: Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé
Publicado en: Chaos: An Interdisciplinary Journal of Nonlinear Science, Edición 29/12, 2019, Página(s) 123112, ISSN 1054-1500
Editor: American Institute of Physics
DOI: 10.1063/1.5100267

Nanoscale coupling of endocytic pit growth and stability

Autores: Martin Lehmann, Ilya Lukonin, Frank Noé, Jan Schmoranzer, Cecilia Clementi, Dinah Loerke, Volker Haucke
Publicado en: Science Advances, Edición 5/11, 2019, Página(s) eaax5775, ISSN 2375-2548
Editor: AAAS
DOI: 10.1126/sciadv.aax5775

Collective hydrogen-bond rearrangement dynamics in liquid water

Autores: R. Schulz, Y. von Hansen, J. O. Daldrop, J. Kappler, F. Noé, R. R. Netz
Publicado en: The Journal of Chemical Physics, Edición 149/24, 2018, Página(s) 244504, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5054267

Efficient multi-objective molecular optimization in a continuous latent space

Autores: Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, Djork-Arné Clevert
Publicado en: Chemical Science, Edición 10/34, 2019, Página(s) 8016-8024, ISSN 2041-6520
Editor: Royal Society of Chemistry
DOI: 10.1039/c9sc01928f

Diffusion-influenced reaction rates in the presence of pair interactions

Autores: Manuel Dibak, Christoph Fröhner, Frank Noé, Felix Höfling
Publicado en: The Journal of Chemical Physics, Edición 151/16, 2019, Página(s) 164105, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5124728

Reversible Interacting-Particle Reaction Dynamics

Autores: Christoph Fröhner, Frank Noé
Publicado en: The Journal of Physical Chemistry B, Edición 122/49, 2018, Página(s) 11240-11250, ISSN 1520-6106
Editor: American Chemical Society
DOI: 10.1021/acs.jpcb.8b06981

Reactive SINDy: Discovering governing reactions from concentration data

Autores: Moritz Hoffmann, Christoph Fröhner, Frank Noé
Publicado en: The Journal of Chemical Physics, Edición 150/2, 2019, Página(s) 025101, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5066099

ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics

Autores: Moritz Hoffmann, Christoph Fröhner, Frank Noé
Publicado en: PLOS Computational Biology, Edición 15/2, 2019, Página(s) e1006830, ISSN 1553-7358
Editor: PLOS
DOI: 10.1371/journal.pcbi.1006830

The mechanism of RNA base fraying: Molecular dynamics simulations analyzed with core-set Markov state models

Autores: Giovanni Pinamonti, Fabian Paul, Frank Noé, Alex Rodriguez, Giovanni Bussi
Publicado en: The Journal of Chemical Physics, Edición 150/15, 2019, Página(s) 154123, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5083227?journalcode=jcp

Markov Models of Molecular Kinetics

Autores: Frank Noé, Edina Rosta
Publicado en: The Journal of Chemical Physics, Edición 151/19, 2019, Página(s) 190401, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5134029

OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics

Autores: David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera, Peter G. Bolhuis
Publicado en: Journal of Chemical Theory and Computation, Edición 15/2, 2018, Página(s) 813-836, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.8b00626

Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat

Autores: Tim Hempel, Lluís Raich, Simon Olsson, Nurit P. Azouz, Andrea M. Klingler, Markus Hoffmann, Stefan Pöhlmann, Marc E. Rothenberg, Frank Noé
Publicado en: Chemical Science, 2021, ISSN 2041-6520
Editor: Royal Society of Chemistry
DOI: 10.1039/d0sc05064d

Large-scale simulation of biomembranes incorporating realistic kinetics into coarse-grained models

Autores: Mohsen Sadeghi, Frank Noé
Publicado en: Nature Communications, Edición 11/1, 2020, ISSN 2041-1723
Editor: Nature Publishing Group
DOI: 10.1038/s41467-020-16424-0

Neural mode jump Monte Carlo

Autores: Luigi Sbailò, Manuel Dibak, Frank Noé
Publicado en: The Journal of Chemical Physics, Edición 154/7, 2021, Página(s) 074101, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/5.0032346

Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures

Autores: Tuan Le, Robin Winter, Frank Noé, Djork-Arné Clevert
Publicado en: Chemical Science, Edición 11/38, 2020, Página(s) 10378-10389, ISSN 2041-6520
Editor: Royal Society of Chemistry
DOI: 10.1039/d0sc03115a

Coupling of Conformational Switches in Calcium Sensor Unraveled with Local Markov Models and Transfer Entropy

Autores: Tim Hempel, Nuria Plattner, Frank Noé
Publicado en: Journal of Chemical Theory and Computation, Edición 16/4, 2020, Página(s) 2584-2593, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.0c00043

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

Autores: Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi
Publicado en: Journal of Chemical Physics, 2020, ISSN 1089-7690
Editor: America Institute of Physics
DOI: 10.1063/5.0007276

Deep learning Markov and Koopman models with physical constraints

Autores: Andreas Mardt, Luca Pasquali, Frank Noé and Hao Wu
Publicado en: Proceedings of The First Mathematical and Scientific Machine Learning Conference, 2020
Editor: PMLR

Deep Generative Markov State Models

Autores: Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
Publicado en: Proceedings of Neural Information Processing Systems (NeurIPS), Edición 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
Editor: -

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Autores: Jonas Köhler, Leon Klein, Frank Noe
Publicado en: 2020
Editor: PMLR

Stochastic Normalizing Flows

Autores: Hao Wu, Jonas Köhler and Frank Noé
Publicado en: 2020
Editor: NeurIPS

Machine Learning for Molecular Dynamics on Long Timescales

Autores: Frank Noé
Publicado en: Machine Learning Meets Quantum Physics, Edición 968, 2020, Página(s) 331-372, ISBN 978-3-030-40244-0
Editor: Springer International Publishing
DOI: 10.1007/978-3-030-40245-7_16

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