Scalable Kinetic Models: From Molecular Dynamics to Cellular Signaling

Deep-neural-network solution of the electronic Schrödinger equation

Autori: Jan Hermann, Zeno Schätzle, Frank Noé
Pubblicato in: Nature Chemistry, Numero 12/10, 2020, Pagina/e 891-897, ISSN 1755-4330
Editore: Nature Publishing Group
DOI: 10.1038/s41557-020-0544-y

TorchMD: A Deep Learning Framework for Molecular Simulations

Autori: Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, Gianni De Fabritiis
Pubblicato in: Journal of Chemical Theory and Computation, Numero 17/4, 2021, Pagina/e 2355-2363, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.0c01343

Machine learning for protein folding and dynamics

Autori: Frank Noé, Gianni De Fabritiis, Cecilia Clementi
Pubblicato in: Current Opinion in Structural Biology, Numero 60, 2020, Pagina/e 77-84, ISSN 0959-440X
Editore: Elsevier BV
DOI: 10.1016/j.sbi.2019.12.005

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

Autori: Z. Schätzle, J. Hermann, F. Noé
Pubblicato in: The Journal of Chemical Physics, Numero 154/12, 2021, Pagina/e 124108, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/5.0032836

Coarse graining molecular dynamics with graph neural networks

Autori: 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
Pubblicato in: The Journal of Chemical Physics, Numero 153/19, 2020, Pagina/e 194101, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/5.0026133

Discovery of a hidden transient state in all bromodomain families

Autori: Lluís Raich, Katharina Meier, Judith Günther, Clara D. Christ, Frank Noé, Simon Olsson
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 118/4, 2021, Pagina/e e2017427118, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.2017427118

Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1

Autori: 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
Pubblicato in: Nature, Numero 571/7765, 2019, Pagina/e 429-433, ISSN 0028-0836
Editore: 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

Autori: Ernesto Suárez, Rafal P. Wiewiora, Chris Wehmeyer, Frank Noé, John D. Chodera, Daniel M. Zuckerman
Pubblicato in: Journal of Chemical Theory and Computation, Numero 17/5, 2021, Pagina/e 3119-3133, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.0c01154

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

Autori: Robin Winter, Floriane Montanari, Frank Noé, Djork-Arné Clevert
Pubblicato in: Chemical Science, Numero 10/6, 2019, Pagina/e 1692-1701, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/c8sc04175j

Machine Learning for Molecular Simulation

Autori: Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Pubblicato in: Annual Review of Physical Chemistry, Numero 71/1, 2020, Pagina/e 361-390, ISSN 0066-426X
Editore: Annual Reviews, Inc.
DOI: 10.1146/annurev-physchem-042018-052331

Stochastic Approximation to MBAR and TRAM: Batchwise Free Energy Estimation

Autori: Maaike M. Galama; Hao Wu; Andreas Krämer; Mohsen Sadeghi; Frank Noé
Pubblicato in: J. Chem. Theory Comput., Numero 19, 2023, Pagina/e 758–766, ISSN 1549-9626
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.2c00976

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

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

Dynamic graphical models of molecular kinetics

Autori: Simon Olsson, Frank Noé
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 116/30, 2019, Pagina/e 15001-15006, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.1901692116

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

Autori: Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu
Pubblicato in: Science, Numero 365/6457, 2019, Pagina/e eaaw1147, ISSN 0036-8075
Editore: American Association for the Advancement of Science
DOI: 10.1126/science.aaw1147

Deflation reveals dynamical structure in nondominant reaction coordinates

Autori: Brooke E. Husic, Frank Noé
Pubblicato in: The Journal of Chemical Physics, Numero 151/5, 2019, Pagina/e 054103, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5099194

Variational selection of features for molecular kinetics

Autori: Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé
Pubblicato in: The Journal of Chemical Physics, Numero 150/19, 2019, Pagina/e 194108, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5083040

Identification of kinetic order parameters for non-equilibrium dynamics

Autori: Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot, Frank Noé
Pubblicato in: The Journal of Chemical Physics, Numero 150/16, 2019, Pagina/e 164120, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5083627

Targeted Adversarial Learning Optimized Sampling

Autori: Jun Zhang, Yi Isaac Yang, Frank Noé
Pubblicato in: The Journal of Physical Chemistry Letters, Numero 10/19, 2019, Pagina/e 5791-5797, ISSN 1948-7185
Editore: American Chemical Society
DOI: 10.1021/acs.jpclett.9b02173

Kernel methods for detecting coherent structures in dynamical data

Autori: Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé
Pubblicato in: Chaos: An Interdisciplinary Journal of Nonlinear Science, Numero 29/12, 2019, Pagina/e 123112, ISSN 1054-1500
Editore: American Institute of Physics
DOI: 10.1063/1.5100267

Nanoscale coupling of endocytic pit growth and stability

Autori: Martin Lehmann, Ilya Lukonin, Frank Noé, Jan Schmoranzer, Cecilia Clementi, Dinah Loerke, Volker Haucke
Pubblicato in: Science Advances, Numero 5/11, 2019, Pagina/e eaax5775, ISSN 2375-2548
Editore: AAAS
DOI: 10.1126/sciadv.aax5775

Collective hydrogen-bond rearrangement dynamics in liquid water

Autori: R. Schulz, Y. von Hansen, J. O. Daldrop, J. Kappler, F. Noé, R. R. Netz
Pubblicato in: The Journal of Chemical Physics, Numero 149/24, 2018, Pagina/e 244504, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5054267

Efficient multi-objective molecular optimization in a continuous latent space

Autori: Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, Djork-Arné Clevert
Pubblicato in: Chemical Science, Numero 10/34, 2019, Pagina/e 8016-8024, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/c9sc01928f

Diffusion-influenced reaction rates in the presence of pair interactions

Autori: Manuel Dibak, Christoph Fröhner, Frank Noé, Felix Höfling
Pubblicato in: The Journal of Chemical Physics, Numero 151/16, 2019, Pagina/e 164105, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5124728

Reversible Interacting-Particle Reaction Dynamics

Autori: Christoph Fröhner, Frank Noé
Pubblicato in: The Journal of Physical Chemistry B, Numero 122/49, 2018, Pagina/e 11240-11250, ISSN 1520-6106
Editore: American Chemical Society
DOI: 10.1021/acs.jpcb.8b06981

Reactive SINDy: Discovering governing reactions from concentration data

Autori: Moritz Hoffmann, Christoph Fröhner, Frank Noé
Pubblicato in: The Journal of Chemical Physics, Numero 150/2, 2019, Pagina/e 025101, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5066099

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

Autori: Moritz Hoffmann, Christoph Fröhner, Frank Noé
Pubblicato in: PLOS Computational Biology, Numero 15/2, 2019, Pagina/e e1006830, ISSN 1553-7358
Editore: PLOS
DOI: 10.1371/journal.pcbi.1006830

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

Autori: Giovanni Pinamonti, Fabian Paul, Frank Noé, Alex Rodriguez, Giovanni Bussi
Pubblicato in: The Journal of Chemical Physics, Numero 150/15, 2019, Pagina/e 154123, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5083227?journalcode=jcp

Markov Models of Molecular Kinetics

Autori: Frank Noé, Edina Rosta
Pubblicato in: The Journal of Chemical Physics, Numero 151/19, 2019, Pagina/e 190401, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5134029

Statistically optimal force aggregation for coarse-graining molecular dynamics

Autori: Andreas Krämer, Aleksander E. P. Durumeric, Nicholas E. Charron, Yaoyi Chen, Cecilia Clementi, and Frank Noé
Pubblicato in: The Journal of Physical Chemistry Letters, Numero 14, 2023, Pagina/e 3970–3979, ISSN 1948-7185
Editore: American Chemical Society
DOI: 10.1021/acs.jpclett.3c00444

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

Autori: David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera, Peter G. Bolhuis
Pubblicato in: Journal of Chemical Theory and Computation, Numero 15/2, 2018, Pagina/e 813-836, ISSN 1549-9618
Editore: 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

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

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

Autori: Mohsen Sadeghi, Frank Noé
Pubblicato in: Nature Communications, Numero 11/1, 2020, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-020-16424-0

Machine learning coarse-grained potentials of protein thermodynamics

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

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

Autori: Patrick Bryant, Atharva Kelkar, Andrea Guljas, Cecilia Clementi and Frank Noé
Pubblicato in: Nature Communications, Numero 15, 2024, Pagina/e 4536, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-024-48837-6

Neural mode jump Monte Carlo

Autori: Luigi Sbailò, Manuel Dibak, Frank Noé
Pubblicato in: The Journal of Chemical Physics, Numero 154/7, 2021, Pagina/e 074101, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/5.0032346

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

Autori: Tuan Le, Robin Winter, Frank Noé, Djork-Arné Clevert
Pubblicato in: Chemical Science, Numero 11/38, 2020, Pagina/e 10378-10389, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/d0sc03115a

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

Autori: Tim Hempel, Nuria Plattner, Frank Noé
Pubblicato in: Journal of Chemical Theory and Computation, Numero 16/4, 2020, Pagina/e 2584-2593, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.0c00043

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

Autori: Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi
Pubblicato in: Journal of Chemical Physics, 2020, ISSN 1089-7690
Editore: America Institute of Physics
DOI: 10.1063/5.0007276

Deep learning Markov and Koopman models with physical constraints

Autori: Andreas Mardt, Luca Pasquali, Frank Noé and Hao Wu
Pubblicato in: Proceedings of The First Mathematical and Scientific Machine Learning Conference, 2020
Editore: PMLR

Deep Generative Markov State Models

Autori: Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
Pubblicato in: Proceedings of Neural Information Processing Systems (NeurIPS), Numero 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
Editore: -

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Autori: Jonas Köhler, Leon Klein, Frank Noe
Pubblicato in: 2020
Editore: PMLR

Stochastic Normalizing Flows

Autori: Hao Wu, Jonas Köhler and Frank Noé
Pubblicato in: 2020
Editore: NeurIPS

Machine Learning for Molecular Dynamics on Long Timescales

Autori: Frank Noé
Pubblicato in: Machine Learning Meets Quantum Physics, Numero 968, 2020, Pagina/e 331-372, ISBN 978-3-030-40244-0
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-40245-7_16

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