Scalable Kinetic Models: From Molecular Dynamics to Cellular Signaling

Deep-neural-network solution of the electronic Schrödinger equation

Autorzy: Jan Hermann, Zeno Schätzle, Frank Noé
Opublikowane w: Nature Chemistry, Numer 12/10, 2020, Strona(/y) 891-897, ISSN 1755-4330
Wydawca: Nature Publishing Group
DOI: 10.1038/s41557-020-0544-y

TorchMD: A Deep Learning Framework for Molecular Simulations

Autorzy: Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, Gianni De Fabritiis
Opublikowane w: Journal of Chemical Theory and Computation, Numer 17/4, 2021, Strona(/y) 2355-2363, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.0c01343

Machine learning for protein folding and dynamics

Autorzy: Frank Noé, Gianni De Fabritiis, Cecilia Clementi
Opublikowane w: Current Opinion in Structural Biology, Numer 60, 2020, Strona(/y) 77-84, ISSN 0959-440X
Wydawca: Elsevier BV
DOI: 10.1016/j.sbi.2019.12.005

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

Autorzy: Z. Schätzle, J. Hermann, F. Noé
Opublikowane w: The Journal of Chemical Physics, Numer 154/12, 2021, Strona(/y) 124108, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/5.0032836

Coarse graining molecular dynamics with graph neural networks

Autorzy: 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
Opublikowane w: The Journal of Chemical Physics, Numer 153/19, 2020, Strona(/y) 194101, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/5.0026133

Discovery of a hidden transient state in all bromodomain families

Autorzy: Lluís Raich, Katharina Meier, Judith Günther, Clara D. Christ, Frank Noé, Simon Olsson
Opublikowane w: Proceedings of the National Academy of Sciences, Numer 118/4, 2021, Strona(/y) e2017427118, ISSN 0027-8424
Wydawca: National Academy of Sciences
DOI: 10.1073/pnas.2017427118

Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1

Autorzy: 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
Opublikowane w: Nature, Numer 571/7765, 2019, Strona(/y) 429-433, ISSN 0028-0836
Wydawca: 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

Autorzy: Ernesto Suárez, Rafal P. Wiewiora, Chris Wehmeyer, Frank Noé, John D. Chodera, Daniel M. Zuckerman
Opublikowane w: Journal of Chemical Theory and Computation, Numer 17/5, 2021, Strona(/y) 3119-3133, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.0c01154

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

Autorzy: Robin Winter, Floriane Montanari, Frank Noé, Djork-Arné Clevert
Opublikowane w: Chemical Science, Numer 10/6, 2019, Strona(/y) 1692-1701, ISSN 2041-6520
Wydawca: Royal Society of Chemistry
DOI: 10.1039/c8sc04175j

Machine Learning for Molecular Simulation

Autorzy: Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Opublikowane w: Annual Review of Physical Chemistry, Numer 71/1, 2020, Strona(/y) 361-390, ISSN 0066-426X
Wydawca: Annual Reviews, Inc.
DOI: 10.1146/annurev-physchem-042018-052331

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

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

Dynamic graphical models of molecular kinetics

Autorzy: Simon Olsson, Frank Noé
Opublikowane w: Proceedings of the National Academy of Sciences, Numer 116/30, 2019, Strona(/y) 15001-15006, ISSN 0027-8424
Wydawca: National Academy of Sciences
DOI: 10.1073/pnas.1901692116

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

Autorzy: Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu
Opublikowane w: Science, Numer 365/6457, 2019, Strona(/y) eaaw1147, ISSN 0036-8075
Wydawca: American Association for the Advancement of Science
DOI: 10.1126/science.aaw1147

Deflation reveals dynamical structure in nondominant reaction coordinates

Autorzy: Brooke E. Husic, Frank Noé
Opublikowane w: The Journal of Chemical Physics, Numer 151/5, 2019, Strona(/y) 054103, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5099194

Variational selection of features for molecular kinetics

Autorzy: Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé
Opublikowane w: The Journal of Chemical Physics, Numer 150/19, 2019, Strona(/y) 194108, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5083040

Identification of kinetic order parameters for non-equilibrium dynamics

Autorzy: Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot, Frank Noé
Opublikowane w: The Journal of Chemical Physics, Numer 150/16, 2019, Strona(/y) 164120, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5083627

Targeted Adversarial Learning Optimized Sampling

Autorzy: Jun Zhang, Yi Isaac Yang, Frank Noé
Opublikowane w: The Journal of Physical Chemistry Letters, Numer 10/19, 2019, Strona(/y) 5791-5797, ISSN 1948-7185
Wydawca: American Chemical Society
DOI: 10.1021/acs.jpclett.9b02173

Kernel methods for detecting coherent structures in dynamical data

Autorzy: Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé
Opublikowane w: Chaos: An Interdisciplinary Journal of Nonlinear Science, Numer 29/12, 2019, Strona(/y) 123112, ISSN 1054-1500
Wydawca: American Institute of Physics
DOI: 10.1063/1.5100267

Nanoscale coupling of endocytic pit growth and stability

Autorzy: Martin Lehmann, Ilya Lukonin, Frank Noé, Jan Schmoranzer, Cecilia Clementi, Dinah Loerke, Volker Haucke
Opublikowane w: Science Advances, Numer 5/11, 2019, Strona(/y) eaax5775, ISSN 2375-2548
Wydawca: AAAS
DOI: 10.1126/sciadv.aax5775

Collective hydrogen-bond rearrangement dynamics in liquid water

Autorzy: R. Schulz, Y. von Hansen, J. O. Daldrop, J. Kappler, F. Noé, R. R. Netz
Opublikowane w: The Journal of Chemical Physics, Numer 149/24, 2018, Strona(/y) 244504, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5054267

Efficient multi-objective molecular optimization in a continuous latent space

Autorzy: Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, Djork-Arné Clevert
Opublikowane w: Chemical Science, Numer 10/34, 2019, Strona(/y) 8016-8024, ISSN 2041-6520
Wydawca: Royal Society of Chemistry
DOI: 10.1039/c9sc01928f

Diffusion-influenced reaction rates in the presence of pair interactions

Autorzy: Manuel Dibak, Christoph Fröhner, Frank Noé, Felix Höfling
Opublikowane w: The Journal of Chemical Physics, Numer 151/16, 2019, Strona(/y) 164105, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5124728

Reversible Interacting-Particle Reaction Dynamics

Autorzy: Christoph Fröhner, Frank Noé
Opublikowane w: The Journal of Physical Chemistry B, Numer 122/49, 2018, Strona(/y) 11240-11250, ISSN 1520-6106
Wydawca: American Chemical Society
DOI: 10.1021/acs.jpcb.8b06981

Reactive SINDy: Discovering governing reactions from concentration data

Autorzy: Moritz Hoffmann, Christoph Fröhner, Frank Noé
Opublikowane w: The Journal of Chemical Physics, Numer 150/2, 2019, Strona(/y) 025101, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5066099

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

Autorzy: Moritz Hoffmann, Christoph Fröhner, Frank Noé
Opublikowane w: PLOS Computational Biology, Numer 15/2, 2019, Strona(/y) e1006830, ISSN 1553-7358
Wydawca: PLOS
DOI: 10.1371/journal.pcbi.1006830

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

Autorzy: Giovanni Pinamonti, Fabian Paul, Frank Noé, Alex Rodriguez, Giovanni Bussi
Opublikowane w: The Journal of Chemical Physics, Numer 150/15, 2019, Strona(/y) 154123, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5083227?journalcode=jcp

Markov Models of Molecular Kinetics

Autorzy: Frank Noé, Edina Rosta
Opublikowane w: The Journal of Chemical Physics, Numer 151/19, 2019, Strona(/y) 190401, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5134029

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

Autorzy: David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera, Peter G. Bolhuis
Opublikowane w: Journal of Chemical Theory and Computation, Numer 15/2, 2018, Strona(/y) 813-836, ISSN 1549-9618
Wydawca: 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

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

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

Autorzy: Mohsen Sadeghi, Frank Noé
Opublikowane w: Nature Communications, Numer 11/1, 2020, ISSN 2041-1723
Wydawca: Nature Publishing Group
DOI: 10.1038/s41467-020-16424-0

Neural mode jump Monte Carlo

Autorzy: Luigi Sbailò, Manuel Dibak, Frank Noé
Opublikowane w: The Journal of Chemical Physics, Numer 154/7, 2021, Strona(/y) 074101, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/5.0032346

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

Autorzy: Tuan Le, Robin Winter, Frank Noé, Djork-Arné Clevert
Opublikowane w: Chemical Science, Numer 11/38, 2020, Strona(/y) 10378-10389, ISSN 2041-6520
Wydawca: Royal Society of Chemistry
DOI: 10.1039/d0sc03115a

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

Autorzy: Tim Hempel, Nuria Plattner, Frank Noé
Opublikowane w: Journal of Chemical Theory and Computation, Numer 16/4, 2020, Strona(/y) 2584-2593, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.0c00043

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

Autorzy: Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi
Opublikowane w: Journal of Chemical Physics, 2020, ISSN 1089-7690
Wydawca: America Institute of Physics
DOI: 10.1063/5.0007276

Deep learning Markov and Koopman models with physical constraints

Autorzy: Andreas Mardt, Luca Pasquali, Frank Noé and Hao Wu
Opublikowane w: Proceedings of The First Mathematical and Scientific Machine Learning Conference, 2020
Wydawca: PMLR

Deep Generative Markov State Models

Autorzy: Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
Opublikowane w: Proceedings of Neural Information Processing Systems (NeurIPS), Numer 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
Wydawca: -

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Autorzy: Jonas Köhler, Leon Klein, Frank Noe
Opublikowane w: 2020
Wydawca: PMLR

Stochastic Normalizing Flows

Autorzy: Hao Wu, Jonas Köhler and Frank Noé
Opublikowane w: 2020
Wydawca: NeurIPS

Machine Learning for Molecular Dynamics on Long Timescales

Autorzy: Frank Noé
Opublikowane w: Machine Learning Meets Quantum Physics, Numer 968, 2020, Strona(/y) 331-372, ISBN 978-3-030-40244-0
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-40245-7_16

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