AA1 – Life Sciences



Data-driven Modeling from Atoms to Cells

Project Heads

Frank Noé, Christof Schütte

Project Members

Simon Olsson (FU)

Project Duration

01.01.2019 – 31.12.2020

Located at

FU Berlin


Living systems are intrinsically multiscalar. No single model can resolve all details and also be computational efficient. This leads to a zoo of different models specialized on different scales. Here we set out to develop a systematic and unified approach for optimal estimation of these models and integrating data from different scales and sources (simulation and experiment).

Project Webpages

Selected Publications

  • J Wang, S Olsson, C Wehmeyer, A Pérez, NE Charron, G De Fabritiis, F. Noé, C. Clementi: Machine learning of coarse-grained molecular dynamics force fields. ACS Central Sci. 5, 755-767 (2019)
  • J Hermann, Z Schätzle, F Noé: Deep neural network solution of the electronic Schrödinger equation. Preprint arXiv:1909.08423 (2019)
  • M Hoffmann, F Noé: Generating valid Euclidean distance matrices. Preprint arXiv:1910.03131 (2019)
  • S Klus, BE Husic, M Mollenhauer, F Noé: Kernel methods for detecting coherent structures in dynamical data. Chaos 29, 123112 (2019)
  • J Köhler, L Klein, F Noé: Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. Preprint arXiv:1910.00753
  • F Noé, S Olsson, J Köhler, H Wu: Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Science 365, eaaw1147 (2019)
  • F Noé, A Tkatchenko, KR Müller, C Clementi: Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71, 361-390 (2020)
  • L Raich, K Meier, J Günther, CD Christ, F Noe, S Olsson: Discovery of a hidden transient state in all bromodomain families. BioRxiv Doi:10.1101/2020.04.01.019547 (2020)
  • F Noé, G De Fabritiis, C Clementi: Machine learning for protein folding and dynamics, Curr. Opin. Struct. Biol. 60, 77-84 (2020)
  • J Wang, S Chmiela, KR Müller, F Noè, C Clementi: Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach. J. Chem. Phys. (in press, preprint arXiv:2005.01851)
  • R Winter, F Montanari, F Noé, DA Clevert: Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chem. Sci. 10, 1692-1701 (2019)
  • R Winter, F Montanari, A Steffen, H Briem, F Noé, DA Clevert: Efficient multi-objective molecular optimization in a continuous latent space. Chem. Sci. 10 (34), 8016-8024 (2019)
  • M Dibak, C Fröhner, F Noé, F Höfling. Diffusion-influenced reaction rates in the presence of pair interactions. J. Chem. Phys. 151, 164105 (2019)
  • F Paul, H Wu, M Vossel, BL de Groot, F Noé: Identification of kinetic order parameters for non-equilibrium dynamics. J. Chem. Phys. 150, 164120 (2019)
  • R Winter, J Retel, F Noé, DA Clevert, A Steffen: grünifai: Interactive multi-parameter optimization of molecules in a continuous vector space. Bioinformatics (2020)

Selected Pictures

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