Scalable Kinetic Models: From Molecular Dynamics to Cellular Signaling

Project Information

ScaleCell

Grant agreement ID: 772230

DOI

10.3030/772230

Project closed

EC signature date 22 January 2018

Start date 1 May 2018

End date 30 April 2024

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 987 500,00

EU contribution

€ 1 987 500,00

Coordinated by

FREIE UNIVERSITAET BERLIN
Germany

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Peer reviewed articles (38)

Deep-neural-network solution of the electronic Schrödinger equation

Author(s): Jan Hermann, Zeno Schätzle, Frank Noé
Published in: Nature Chemistry, Issue 12/10, 2020, Page(s) 891-897, ISSN 1755-4330
Publisher: Nature Publishing Group
DOI: 10.1038/s41557-020-0544-y

TorchMD: A Deep Learning Framework for Molecular Simulations

Author(s): Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, Gianni De Fabritiis
Published in: Journal of Chemical Theory and Computation, Issue 17/4, 2021, Page(s) 2355-2363, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.0c01343

Machine learning for protein folding and dynamics

Author(s): Frank Noé, Gianni De Fabritiis, Cecilia Clementi
Published in: Current Opinion in Structural Biology, Issue 60, 2020, Page(s) 77-84, ISSN 0959-440X
Publisher: Elsevier BV
DOI: 10.1016/j.sbi.2019.12.005

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

Author(s): Z. Schätzle, J. Hermann, F. Noé
Published in: The Journal of Chemical Physics, Issue 154/12, 2021, Page(s) 124108, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0032836

Coarse graining molecular dynamics with graph neural networks

Author(s): 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
Published in: The Journal of Chemical Physics, Issue 153/19, 2020, Page(s) 194101, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0026133

Discovery of a hidden transient state in all bromodomain families

Author(s): Lluís Raich, Katharina Meier, Judith Günther, Clara D. Christ, Frank Noé, Simon Olsson
Published in: Proceedings of the National Academy of Sciences, Issue 118/4, 2021, Page(s) e2017427118, ISSN 0027-8424
Publisher: National Academy of Sciences
DOI: 10.1073/pnas.2017427118

Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1

Author(s): 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
Published in: Nature, Issue 571/7765, 2019, Page(s) 429-433, ISSN 0028-0836
Publisher: 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

Author(s): Ernesto Suárez, Rafal P. Wiewiora, Chris Wehmeyer, Frank Noé, John D. Chodera, Daniel M. Zuckerman
Published in: Journal of Chemical Theory and Computation, Issue 17/5, 2021, Page(s) 3119-3133, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.0c01154

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

Author(s): Robin Winter, Floriane Montanari, Frank Noé, Djork-Arné Clevert
Published in: Chemical Science, Issue 10/6, 2019, Page(s) 1692-1701, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/c8sc04175j

Machine Learning for Molecular Simulation

Author(s): Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Published in: Annual Review of Physical Chemistry, Issue 71/1, 2020, Page(s) 361-390, ISSN 0066-426X
Publisher: Annual Reviews, Inc.
DOI: 10.1146/annurev-physchem-042018-052331

Stochastic Approximation to MBAR and TRAM: Batchwise Free Energy Estimation

Author(s): Maaike M. Galama; Hao Wu; Andreas Krämer; Mohsen Sadeghi; Frank Noé
Published in: J. Chem. Theory Comput., Issue 19, 2023, Page(s) 758–766, ISSN 1549-9626
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.2c00976

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Author(s): Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
Published in: ACS Central Science, 2019, ISSN 2374-7943
Publisher: American Chemical Society
DOI: 10.1021/acscentsci.8b00913

Dynamic graphical models of molecular kinetics

Author(s): Simon Olsson, Frank Noé
Published in: Proceedings of the National Academy of Sciences, Issue 116/30, 2019, Page(s) 15001-15006, ISSN 0027-8424
Publisher: National Academy of Sciences
DOI: 10.1073/pnas.1901692116

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

Author(s): Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu
Published in: Science, Issue 365/6457, 2019, Page(s) eaaw1147, ISSN 0036-8075
Publisher: American Association for the Advancement of Science
DOI: 10.1126/science.aaw1147

Deflation reveals dynamical structure in nondominant reaction coordinates

Author(s): Brooke E. Husic, Frank Noé
Published in: The Journal of Chemical Physics, Issue 151/5, 2019, Page(s) 054103, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5099194

Variational selection of features for molecular kinetics

Author(s): Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé
Published in: The Journal of Chemical Physics, Issue 150/19, 2019, Page(s) 194108, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5083040

Identification of kinetic order parameters for non-equilibrium dynamics

Author(s): Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot, Frank Noé
Published in: The Journal of Chemical Physics, Issue 150/16, 2019, Page(s) 164120, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5083627

Targeted Adversarial Learning Optimized Sampling

Author(s): Jun Zhang, Yi Isaac Yang, Frank Noé
Published in: The Journal of Physical Chemistry Letters, Issue 10/19, 2019, Page(s) 5791-5797, ISSN 1948-7185
Publisher: American Chemical Society
DOI: 10.1021/acs.jpclett.9b02173

Kernel methods for detecting coherent structures in dynamical data

Author(s): Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé
Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science, Issue 29/12, 2019, Page(s) 123112, ISSN 1054-1500
Publisher: American Institute of Physics
DOI: 10.1063/1.5100267

Nanoscale coupling of endocytic pit growth and stability

Author(s): Martin Lehmann, Ilya Lukonin, Frank Noé, Jan Schmoranzer, Cecilia Clementi, Dinah Loerke, Volker Haucke
Published in: Science Advances, Issue 5/11, 2019, Page(s) eaax5775, ISSN 2375-2548
Publisher: AAAS
DOI: 10.1126/sciadv.aax5775

Collective hydrogen-bond rearrangement dynamics in liquid water

Author(s): R. Schulz, Y. von Hansen, J. O. Daldrop, J. Kappler, F. Noé, R. R. Netz
Published in: The Journal of Chemical Physics, Issue 149/24, 2018, Page(s) 244504, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5054267

Efficient multi-objective molecular optimization in a continuous latent space

Author(s): Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, Djork-Arné Clevert
Published in: Chemical Science, Issue 10/34, 2019, Page(s) 8016-8024, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/c9sc01928f

Diffusion-influenced reaction rates in the presence of pair interactions

Author(s): Manuel Dibak, Christoph Fröhner, Frank Noé, Felix Höfling
Published in: The Journal of Chemical Physics, Issue 151/16, 2019, Page(s) 164105, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5124728

Reversible Interacting-Particle Reaction Dynamics

Author(s): Christoph Fröhner, Frank Noé
Published in: The Journal of Physical Chemistry B, Issue 122/49, 2018, Page(s) 11240-11250, ISSN 1520-6106
Publisher: American Chemical Society
DOI: 10.1021/acs.jpcb.8b06981

Reactive SINDy: Discovering governing reactions from concentration data

Author(s): Moritz Hoffmann, Christoph Fröhner, Frank Noé
Published in: The Journal of Chemical Physics, Issue 150/2, 2019, Page(s) 025101, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5066099

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

Author(s): Moritz Hoffmann, Christoph Fröhner, Frank Noé
Published in: PLOS Computational Biology, Issue 15/2, 2019, Page(s) e1006830, ISSN 1553-7358
Publisher: PLOS
DOI: 10.1371/journal.pcbi.1006830

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

Author(s): Giovanni Pinamonti, Fabian Paul, Frank Noé, Alex Rodriguez, Giovanni Bussi
Published in: The Journal of Chemical Physics, Issue 150/15, 2019, Page(s) 154123, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5083227?journalcode=jcp

Markov Models of Molecular Kinetics

Author(s): Frank Noé, Edina Rosta
Published in: The Journal of Chemical Physics, Issue 151/19, 2019, Page(s) 190401, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5134029

Statistically optimal force aggregation for coarse-graining molecular dynamics

Author(s): Andreas Krämer, Aleksander E. P. Durumeric, Nicholas E. Charron, Yaoyi Chen, Cecilia Clementi, and Frank Noé
Published in: The Journal of Physical Chemistry Letters, Issue 14, 2023, Page(s) 3970–3979, ISSN 1948-7185
Publisher: American Chemical Society
DOI: 10.1021/acs.jpclett.3c00444

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

Author(s): David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera, Peter G. Bolhuis
Published in: Journal of Chemical Theory and Computation, Issue 15/2, 2018, Page(s) 813-836, ISSN 1549-9618
Publisher: 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

Author(s): Tim Hempel, Lluís Raich, Simon Olsson, Nurit P. Azouz, Andrea M. Klingler, Markus Hoffmann, Stefan Pöhlmann, Marc E. Rothenberg, Frank Noé
Published in: Chemical Science, 2021, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/d0sc05064d

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

Author(s): Mohsen Sadeghi, Frank Noé
Published in: Nature Communications, Issue 11/1, 2020, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-020-16424-0

Machine learning coarse-grained potentials of protein thermodynamics

Author(s): Majewski; Maciej; Pérez; Adrià; Thölke; Philipp; Doerr; Stefan; Charron; Nicholas E.; Giorgino; Toni; Husic; Brooke E.; Clementi; Cecilia; Noe; Frank; De Fabritiis; Gianni
Published in: Nature communications 14 (2023): 1–13. doi:10.1038/s41467-023-41343-1, Issue 14, 2023, Page(s) 5739, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-023-41343-1

Structure prediction of protein-ligand complexes from sequence information with Umol

Author(s): Patrick Bryant, Atharva Kelkar, Andrea Guljas, Cecilia Clementi and Frank Noé
Published in: Nature Communications, Issue 15, 2024, Page(s) 4536, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-024-48837-6

Neural mode jump Monte Carlo

Author(s): Luigi Sbailò, Manuel Dibak, Frank Noé
Published in: The Journal of Chemical Physics, Issue 154/7, 2021, Page(s) 074101, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0032346

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

Author(s): Tuan Le, Robin Winter, Frank Noé, Djork-Arné Clevert
Published in: Chemical Science, Issue 11/38, 2020, Page(s) 10378-10389, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/d0sc03115a

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

Author(s): Tim Hempel, Nuria Plattner, Frank Noé
Published in: Journal of Chemical Theory and Computation, Issue 16/4, 2020, Page(s) 2584-2593, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.0c00043

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

Author(s): Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi
Published in: Journal of Chemical Physics, 2020, ISSN 1089-7690
Publisher: America Institute of Physics
DOI: 10.1063/5.0007276

Conference proceedings (4)

Deep learning Markov and Koopman models with physical constraints

Author(s): Andreas Mardt, Luca Pasquali, Frank Noé and Hao Wu
Published in: Proceedings of The First Mathematical and Scientific Machine Learning Conference, 2020
Publisher: PMLR

Deep Generative Markov State Models

Author(s): Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
Published in: Proceedings of Neural Information Processing Systems (NeurIPS), Issue 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
Publisher: -

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Author(s): Jonas Köhler, Leon Klein, Frank Noe
Published in: 2020
Publisher: PMLR

Stochastic Normalizing Flows

Author(s): Hao Wu, Jonas Köhler and Frank Noé
Published in: 2020
Publisher: NeurIPS

Book chapters (1)

Machine Learning for Molecular Dynamics on Long Timescales

Author(s): Frank Noé
Published in: Machine Learning Meets Quantum Physics, Issue 968, 2020, Page(s) 331-372, ISBN 978-3-030-40244-0
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-40245-7_16