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Frank Noé

Active 2006–2026

183
Papers
25,210
Citations
75
h-index
149
i10-index

Citations

Citations per year for Frank Noé1937: 1 citations1989: 2 citations1992: 1 citations1995: 2 citations1996: 2 citations1998: 1 citations1999: 5 citations2003: 1 citations2004: 1 citations2006: 3 citations2007: 12 citations2008: 21 citations2009: 28 citations2010: 51 citations2011: 103 citations2012: 105 citations2013: 128 citations2014: 182 citations2015: 191 citations2016: 241 citations2017: 319 citations2018: 399 citations2019: 853 citations2020: 1,066 citations2021: 1,374 citations2022: 1,121 citations2023: 1,104 citations2024: 1,342 citations2025: 1,066 citations2026: 221 citations1938–1988: no citations, so these years are not shown1990–1991: no citations, so these years are not shown1993–1994: no citations, so these years are not shown1997: no citations, so this year is not shown2000–2002: no citations, so these years are not shown2005: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 2,245 citing papers, 27% of this breakdownGermany: 900 citing papers, 10.8% of this breakdownChina: 783 citing papers, 9.4% of this breakdownUnited Kingdom: 595 citing papers, 7.2% of this breakdownSwitzerland: 284 citing papers, 3.4% of this breakdownItaly: 278 citing papers, 3.4% of this breakdownFrance: 277 citing papers, 3.3% of this breakdownJapan: 219 citing papers, 2.6% of this breakdownSpain: 203 citing papers, 2.5% of this breakdownIndia: 200 citing papers, 2.4% of this breakdownCanada: 199 citing papers, 2.4% of this breakdownNetherlands: 150 citing papers, 1.8% of this breakdown
0%27%Other 23.8%

Institutions

Fields

  • Biochemistry, Genetics and Molecular Biology39.2%
  • Computer Science19.3%
  • Materials Science11.4%
  • Medicine9.8%
  • Physics and Astronomy8.1%
  • Engineering3%
  • Other9.2%

Topics

  • Protein Structure and Dynamics11%
  • Computational Drug Discovery Methods8%
  • Machine Learning in Materials Science7%
  • Enzyme Structure and Function2.2%
  • Model Reduction and Neural Networks2.1%
  • RNA and protein synthesis mechanisms1.9%
  • Other67.8%

Coauthors

All papers

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  1. PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models

    Authors: Martin K. Scherer, Benjamin Trendelkamp-Schroer, Fabian Paul, Guillermo Pérez‐Hernández, Moritz Hoffmann, Nuria Plattner, Christoph Wehmeyer, Jan-Hendrik Prinz, Frank Noé - Journal of Chemical Theory and Computation 2015 cited by 1,299

  2. Machine learning for molecular simulation

    Authors: Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi - Annual Review of Physical Chemistry 2020 cited by 888

  3. Identification of slow molecular order parameters for Markov model construction

    Authors: Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, Frank Noé - The Journal of Chemical Physics 2013 cited by 1,049

  4. Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: Conformational Plasticity in Antigen Presentation

    Authors: Marek Wieczorek, Esam T. Abualrous, Jana Sticht, Miguel Álvaro‐Benito, Sebastian Stolzenberg, Frank Noé, Christian Freund - Frontiers in Immunology 2017 cited by 1,011

  5. Fast protein backbone generation with SE(3) flow matching

    Authors: Jason Yim, Andrew M. Campbell, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Víctor García Satorras, Bastiaan S. Veeling, Regina Barzilay, Tommi Jaakkola, Frank Noé - arXiv (Cornell University) 2023 cited by 103

  6. Markov models of molecular kinetics: Generation and validation

    Authors: Jan-Hendrik Prinz, Hao Wu, Marco Sarich, Bettina G. Keller, Martin Senne, Martin Held, John D. Chodera, Christof Schütte, Frank Noé - The Journal of Chemical Physics 2011 cited by 1,347

  7. Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations

    Authors: Robin Winter, Floriane Montanari, Frank Noé, Djork-Arné Clevert - Chemical Science 2018 cited by 517

  8. VAMPnets for deep learning of molecular kinetics

    Authors: Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé - Nature Communications 2017 cited by 527

  9. Unsupervised Learning Methods for Molecular Simulation Data

    Authors: Aldo Glielmo, Brooke E. Husic, Álex Rodríguez, Cecilia Clementi, Frank Noé, Alessandro Laio - Chemical Reviews 2021 cited by 407

  10. Deep neural network solution of the electronic Schrödinger equation

    Authors: Jan Hermann, Zeno Schätzle, Frank Noé - Nature Chemistry 2020 cited by 471

  11. Predicting equilibrium distributions for molecular systems with deep learning

    Authors: Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu - Nature Machine Intelligence, Nat. Mac. Intell. 2024 cited by 127

  12. Machine Learning of coarse-grained Molecular Dynamics Force Fields

    Authors: Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E. Charron, Gianni De Fabritiis, Frank Noé, Cecilia Clementi - ACS Central Science 2019 cited by 525

  13. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery

    Authors: Stefan Doerr, M J Harvey, Frank Noé, Gianni De Fabritiis - Journal of Chemical Theory and Computation 2016 cited by 491

  14. Markov state models of biomolecular conformational dynamics

    Authors: John D. Chodera, Frank Noé - Current Opinion in Structural Biology 2014 cited by 836

  15. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

    Authors: Christoph Wehmeyer, Frank Noé - The Journal of Chemical Physics 2018 cited by 412

  16. Efficient Multi-Objective Molecular Optimization in a Continuous Latent Space

    Authors: Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, Djork-Arné Clevert - Chemical Science 2019 cited by 279

  17. Scalable emulation of protein equilibrium ensembles with generative deep learning

    Authors: Sarah Lewis, Tim Hempel, José Jiménez-Luna, Michael Gastegger, Yu Xie, Andrew Y. K. Foong, Víctor García Satorras, Osama Abdin, Bastiaan S. Veeling, Iryna Zaporozhets, Yaoyi Chen, Soojung Yang, Adam Foster, Arne Schneuing, Jigyasa Nigam, Federico Barbero, Vincent Stimper, Andrew M. Campbell, Jason Yim, Marten Lienen, Yu Shi, Shuxin Zheng, Hannes Schulz, Usman Munir, Roberto Sordillo, Ryota Tomioka, Cecilia Clementi, Frank Noé - Science 2025 cited by 221

  18. Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity

    Authors: Markus Hoffmann, Heike Hofmann-Winkler, Joan C. Smith, Nadine Krüger, Prerna Arora, Lambert K. Sørensen, Ole S. Søgaard, Jørgen Bo Hasselstrøm, Michael Winkler, Tim Hempel, Lluı́s Raich, Simon Olsson, Olga Danov, Danny Jonigk, Takashi Yamazoe, Katsura Yamatsuta, Hirotaka Mizuno, Stephan Ludwig, Frank Noé, Mads Kjølby, Armin Braun, Jason M. Sheltzer, Stefan Pöhlmann - EBioMedicine 2021 cited by 398

  19. Structure prediction of alternative protein conformations

    Authors: Patrick Bryant, Frank Noé - Nature Communications 2024 cited by 94

  20. Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

    Authors: Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu - arXiv (Cornell University), CoRR 2023 cited by 44

  21. Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations

    Authors: Frank Noé, Christof Schütte, Eric Vanden‐Eijnden, Lothar Reich, Thomas R. Weikl - National Academy of Sciences, Proceedings of the National Academy of Sciences 2009 cited by 851

  22. Data-Driven Model Reduction and Transfer Operator Approximation

    Authors: Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis G. Kevrekidis, Christof Schütte, Frank Noé - Journal of Nonlinear Science, J. Nonlinear Sci. 2018 cited by 259

  23. Scalable emulation of protein equilibrium ensembles with generative deep learning

    Authors: Sarah Lewis, Tim Hempel, José Jiménez-Luna, Michael Gastegger, Yu Xie, Andrew Y. K. Foong, Víctor García Satorras, Osama Abdin, Bastiaan S. Veeling, Iryna Zaporozhets, Yaoyi Chen, Soojung Yang, Arne Schneuing, Jigyasa Nigam, Federico Barbero, Vincent Stimper, Andrew M. Campbell, Jason Yim, Marten Lienen, Yu Shi, Shuxin Zheng, Hannes Schulz, Usman Munir, Ryota Tomioka, Cecilia Clementi, Frank Noé - 2024 cited by 60

  24. Equivariant Flows: sampling configurations for multi-body systems with symmetric energies

    Authors: Jonas Köhler, Leon Klein, Frank Noé - arXiv (Cornell University), CoRR 2019 cited by 71