publications

2024

1. Efficient 3D Molecular Generation with Flow Matching and Scale Optimal Transport

   Irwin, Ross, Tibo, Alessandro, Janet, Jon Paul, and Olsson, Simon

   2024
2. Generation of conformational ensembles of small molecules via Surrogate Model-Assisted Molecular Dynamics

   Viguera Diez, Juan, Romeo Atance, Sara, Engkvist, Ola, and Olsson, Simon

   2024

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3. Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems

   Kolloff, Christopher, and Olsson, Simon

   2024

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2023

1. Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics

   Schreiner, Mathias, Winther, Ole, and Olsson, Simon

   In Thirty-seventh Conference on Neural Information Processing Systems 2023

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2. Rescuing Off-Equilibrium Simulation Data through Dynamic Experimental Data with dynAMMo

   Kolloff, Christopher, and Olsson, Simon

   Machine Learning: Science and Technology 2023

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3. Navigating protein landscapes with a machine-learned transferable coarse-grained model

   Clementi, Cecilia, Charron, Nicholas, Musil, Felix, Guljas, Andrea, Chen, Yaoyi, Bonneau, Klara, Pasos-Trejo, Aldo, Venturin, Jacopo, Gusew, Daria, Zaporozhets, Iryna, Krämer, Andreas, Templeton, Clark, Kelkar, Atharva, Durumeric, Aleksander, Olsson, Simon, Pérez, Adrià, Majewski, Maciej, Husic, Brooke, Patel, Ankit, De Fabritiis, Gianni, and Noé, Frank

   2023

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4. A design strategy to generate a SARS-CoV-2 RBD vaccine that abrogates ACE2 binding and improves neutralizing antibody responses

   Ratswohl, Christoph, Vázquez García, Clara, Ahmad, Ata ul Wakeel, Gonschior, Hannes, Lebedin, Mikhail, Silvis, Casper Ewijn, Spatt, Lisa, Gerhard, Cathrin, Lehmann, Martin, Sander, Leif E., Kurth, Florian, Olsson, Simon, and Rosa, Kathrin

   European Journal of Immunology 2023

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5. Vendi sampling for molecular simulations: Diversity as a force for faster convergence and better exploration

   Pasarkar, Amey P., Bencomo, Gianluca M., Olsson, Simon, and Dieng, Adji Bousso

   The Journal of Chemical Physics 2023

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6. A community effort to discover small molecule SARS-CoV-2 inhibitors

   Schimunek, Johannes, Seidl, Philipp, Elez, Katarina, Hempel, Tim, Le, Tuan, Noé, Frank, Olsson, Simon, Raich, Lluı́s, Winter, Robin, Gokcan, Hatice, Gusev, Filipp, Gutkin, Evgeny M., Isayev, Olexandr, Kurnikova, Maria G., Narangoda, Chamali H., Zubatyuk, Roman, Bosko, Ivan P., Furs, Konstantin V., Karpenko, Anna D., Kornoushenko, Yury V., Shuldau, Mikita, Yushkevich, Artsemi, Benabderrahmane, Mohammed B., Bousquet-Melou, Patrick, Bureau, Ronan, Charton, Beatrice, Cirou, Bertrand C., Gil, Gérard, Allen, William J., Sirimulla, Suman, Watowich, Stanley, Antonopoulos, Nick A., Epitropakis, Nikolaos E., Krasoulis, Agamemnon K., Pitsikalis, Vassilis P., Theodorakis, Stavros T., Kozlovskii, Igor, Maliutin, Anton, Medvedev, Alexander, Popov, Petr, Zaretckii, Mark, Eghbal-zadeh, Hamid, Halmich, Christina, Hochreiter, Sepp, Mayr, Andreas, Ruch, Peter, Widrich, Michael, Berenger, Francois, Kumar, Ashutosh, Yamanishi, Yoshihiro, Zhang, Kam Y.J., Bengio, Emmanuel, Bengio, Yoshua, Jain, Moksh J., Korablyov, Maksym, Liu, Cheng-Hao, Marcou, Gilles, Glaab, Enrico, Barnsley, Kelly, Iyengar, Suhasini M., Ondrechen, Mary Jo, Haupt, V. Joachim, Kaiser, Florian, Schroeder, Michael, Pugliese, Luisa, Albani, Simone, Athanasiou, Christina, Beccari, Andrea, Carloni, Paolo, D’Arrigo, Giulia, Gianquinto, Eleonora, Goßen, Jonas, Hanke, Anton, Joseph, Benjamin P., Kokh, Daria B., Kovachka, Sandra, Manelfi, Candida, Mukherjee, Goutam, Muñiz-Chicharro, Abraham, Musiani, Francesco, Nunes-Alves, Ariane, Paiardi, Giulia, Rossetti, Giulia, Sadiq, S. Kashif, Spyrakis, Francesca, Talarico, Carmine, Tsengenes, Alexandros, Wade, Rebecca C., Copeland, Conner, Gaiser, Jeremiah, Olson, Daniel R., Roy, Amitava, Venkatraman, Vishwesh, Wheeler, Travis J., Arthanari, Haribabu, Blaschitz, Klara, Cespugli, Marco, Durmaz, Vedat, Fackeldey, Konstantin, Fischer, Patrick D., Gorgulla, Christoph, Gruber, Christian, Gruber, Karl, Hetmann, Michael, Kinney, Jamie E., Das, Krishna M. Padmanabha, Pandita, Shreya, Singh, Amit, Steinkellner, Georg, Tesseyre, Guilhem, Wagner, Gerhard, Wang, Zi-Fu, Yust, Ryan J., Druzhilovskiy, Dmitry S., Filimonov, Dmitry A., Pogodin, Pavel V., Poroikov, Vladimir, Rudik, Anastassia V., Stolbov, Leonid A., Veselovsky, Alexander V., Rosa, Maria De, Simone, Giada De, Gulotta, Maria R., Lombino, Jessica, Mekni, Nedra, Perricone, Ugo, Casini, Arturo, Embree, Amanda, Gordon, D. Benjamin, Lei, David, Pratt, Katelin, Voigt, Christopher A., Chen, Kuang-Yu, Jacob, Yves, Krischuns, Tim, Lafaye, Pierre, Zettor, Agnès, Rodrı́guez, M. Luis, White, Kris M., Fearon, Daren, Delft, Frank Von, Walsh, Martin A., Horvath, Dragos, III, Charles L. Brooks, Falsafi, Babak, Ford, Bryan, Garcı́a-Sastre, Adolfo, Lee, Sang Yup, Naffakh, Nadia, Varnek, Alexandre, Klambauer, Günter, and Hermans, Thomas M.

   2023

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2022

1. Markov field models: Scaling molecular kinetics approaches to large molecular machines

   Hempel, Tim, Olsson, Simon, and Noé, Frank

   Current Opinion in Structural Biology 2022

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   With recent advances in structural biology, including experimental techniques and deep learning-enabled high-precision structure predictions, molecular dynamics methods that scale up to large biomolecular systems are required. Current state-of-the-art approaches in molecular dynamics modeling focus on encoding global configurations of molecular systems as distinct states. This paradigm commands us to map out all possible structures and sample transitions between them, a task that becomes impossible for large-scale systems such as biomolecular complexes. To arrive at scalable molecular models, we suggest moving away from global state descriptions to a set of coupled models that each describe the dynamics of local domains or sites of the molecular system. We describe limitations in the current state-of-the-art global-state Markovian modeling approaches and then introduce Markov field models as an umbrella term that includes models from various scientific communities, including Independent Markov decomposition, Ising and Potts models, and (dynamic) graphical models, and evaluate their use for computational molecular biology. Finally, we give a few examples of early adoptions of these ideas for modeling molecular kinetics and thermodynamics.

2. Markov State Models of Protein-Protein Encounters

   Olsson, Simon

   2022

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3. A litmus test for classifying recognition mechanisms of transiently binding proteins

   Chakrabarti, Kalyan S., Olsson, Simon, Pratihar, Supriya, Giller, Karin, Overkamp, Kerstin, Lee, Ko On, Gapsys, Vytautas, Ryu, Kyoung-Seok, Groot, Bert L., Noé, Frank, Becker, Stefan, Lee, Donghan, Weikl, Thomas R., and Griesinger, Christian

   Nature Communications 2022

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4. Motional clustering in supra-\tau_c conformational exchange influences NOE cross-relaxation rate

   Kolloff, Christopher, Mazur, Adam, Marzinek, Jan K., Bond, Peter J., Olsson, Simon, and Hiller, Sebastian

   Journal of Magnetic Resonance 2022

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5. Prediction of the chemical context for Buchwald-Hartwig coupling reactions

   Genheden, Samuel, Mårdh, Agnes, Lahti, Gustav, Engkvist, Ola, Olsson, Simon, and Kogej, Thierry

   Molecular Informatics 2022

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6. De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models

   Atance, Sara Romeo, Diez, Juan Viguera, Engkvist, Ola, Olsson, Simon, and Mercado, Rocío

   Journal of Chemical Information and Modeling 2022

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2021

1. A transferable Boltzmann generator for small-molecules conformers.

   Diez, Juan Viguera, Atance, Sara Romeo, Engkvist, Ola, Mercado, Rocı́o, and Olsson, Simon

   ELLIS Machine Learning for Molecule Discovery Workshop (ML4Molecules) 2021 2021
2. Multi-body effects in a coarse-grained protein force field

   Wang, Jiang, Charron, Nicholas, Husic, Brooke, Olsson, Simon, Noé, Frank, and Clementi, Cecilia

   The Journal of Chemical Physics 2021

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

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

   EBioMedicine 2021

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4. Discovery of a hidden transient state in all bromodomain families

   Raich, Lluı́s, Meier, Katharina, Günther, Judith, Christ, Clara D., Noé, Frank, and Olsson, Simon

   Proceedings of the National Academy of Sciences 2021

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5. Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat

   Hempel, Tim, Raich, Lluı́s, Olsson, Simon, Azouz, Nurit P., Klingler, Andrea M., Hoffmann, Markus, Pöhlmann, Stefan, Rothenberg, Marc E., and Noé, Frank

   Chemical Science 2021

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2020

1. Coarse graining molecular dynamics with graph neural networks

   Husic, Brooke E., Charron, Nicholas E., Lemm, Dominik, Wang, Jiang, Pérez, Adrià, Majewski, Maciej, Krämer, Andreas, Chen, Yaoyi, Olsson, Simon, Fabritiis, Gianni, Noé, Frank, and Clementi, Cecilia

   The Journal of Chemical Physics 2020

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2. Protein Allostery at Atomic Resolution

   Strotz, Dean, Orts, Julien, Kadavath, Harindranath, Friedmann, Michael, Ghosh, Dhiman, Olsson, Simon, Chi, Celestine N., Pokharna, Aditya, Güntert, Peter, Vögeli, Beat, and Riek, Roland

   Angewandte Chemie International Edition 2020

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2019

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

   Noé, Frank, Olsson, Simon, Köhler, Jonas, and Wu, Hao

   Science 2019

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2. Dynamic graphical models of molecular kinetics

   Olsson, Simon, and Noé, Frank

   Proceedings of the National Academy of Sciences 2019

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3. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

   Wang, Jiang, Olsson, Simon, Wehmeyer, Christoph, Pérez, Adrià, Charron, Nicholas E., Fabritiis, Gianni, Noé, Frank, and Clementi, Cecilia

   ACS Central Science 2019

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4. Introduction to Markov state modeling with the PyEMMA software [Article v1.0]

   Wehmeyer, Christoph, Scherer, Martin K., Hempel, Tim, Husic, Brooke E., Olsson, Simon, and Noé, Frank

   Living Journal of Computational Molecular Science 2019

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2018

1. A scalable approach to the computation of invariant measures for high-dimensional Markovian systems

   Gerber, Susanne, Olsson, Simon, Noé, Frank, and Horenko, Illia

   Scientific Reports 2018

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2017

1. The Exact Nuclear Overhauser Enhancement: Recent Advances

   Nichols, Parker, Born, Alexandra, Henen, Morkos, Strotz, Dean, Orts, Julien, Olsson, Simon, Güntert, Peter, Chi, Celestine, and Vögeli, Beat

   Molecules 2017

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2. Combining experimental and simulation data of molecular processes via augmented Markov models

   Olsson, Simon, Wu, Hao, Paul, Fabian, Clementi, Cecilia, and Noé, Frank

   Proceedings of the National Academy of Sciences 2017

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2016

1. Mechanistic Models of Chemical Exchange Induced Relaxation in Protein NMR

   Olsson, Simon, and Noé, Frank

   Journal of the American Chemical Society 2016

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2. The Dynamic Basis for Signal Propagation in Human Pin1-WW

   Olsson, Simon, Strotz, Dean, Vögeli, Beat, Riek, Roland, and Cavalli, Andrea

   Structure 2016

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3. Bayesian inference of protein ensembles from SAXS data

   Antonov, L. D., Olsson, S., Boomsma, W., and Hamelryck, T.

   Physical Chemistry Chemical Physics 2016

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4. The Exact NOE as an Alternative in Ensemble Structure Determination

   Vögeli, Beat, Olsson, Simon, Güntert, Peter, and Riek, Roland

   Biophysical Journal 2016

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2015

1. Molecular Determinants for Unphosphorylated STAT3 Dimerization Determined by Integrative Modeling

   Sgrignani, Jacopo, Olsson, Simon, Ekonomiuk, Dariusz, Genini, Davide, Krause, Rolf, Catapano, Carlo V., and Cavalli, Andrea

   Biochemistry 2015

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2. Quantification of Entropy-Loss in Replica-Averaged Modeling

   Olsson, Simon, and Cavalli, Andrea

   Journal of Chemical Theory and Computation 2015

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3. Compiled data set of exact NOE distance limits, residual dipolar couplings and scalar couplings for the protein GB3

   Vögeli, Beat, Olsson, Simon, Riek, Roland, and Güntert, Peter

   Data in Brief 2015

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4. Complementarity and congruence between exact NOEs and traditional NMR probes for spatial decoding of protein dynamics

   Vögeli, Beat, Olsson, Simon, Riek, Roland, and Güntert, Peter

   Journal of Structural Biology 2015

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5. Molecular Dynamics of Biomolecules through Direct Analysis of Dipolar Couplings

   Olsson, Simon, Ekonomiuk, Dariusz, Sgrignani, Jacopo, and Cavalli, Andrea

   Journal of the American Chemical Society 2015

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2014

1. Probabilistic Determination of Native State Ensembles of Proteins

   Olsson, Simon, Vögeli, Beat Rolf, Cavalli, Andrea, Boomsma, Wouter, Ferkinghoff-Borg, Jesper, Lindorff-Larsen, Kresten, and Hamelryck, Thomas

   Journal of Chemical Theory and Computation 2014

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2013

1. Inference of Structure Ensembles of Flexible Biomolecules from Sparse, Averaged Data

   Olsson, Simon, Frellsen, Jes, Boomsma, Wouter, Mardia, Kanti V., and Hamelryck, Thomas

   PLoS ONE 2013

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2. PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure

   Boomsma, Wouter, Frellsen, Jes, Harder, Tim, Bottaro, Sandro, Johansson, Kristoffer E., Tian, Pengfei, Stovgaard, Kasper, Andreetta, Christian, Olsson, Simon, Valentin, Jan B., Antonov, Lubomir D., Christensen, Anders S., Borg, Mikael, Jensen, Jan H., Lindorff-Larsen, Kresten, Ferkinghoff-Borg, Jesper, and Hamelryck, Thomas

   Journal of Computational Chemistry 2013

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2012

1. An Efficient Null Model for Conformational Fluctuations in Proteins

   Harder, Tim, Borg, Mikael, Bottaro, Sandro, Boomsma, Wouter, Olsson, Simon, Ferkinghoff-Borg, Jesper, and Hamelryck, Thomas

   Structure 2012

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2011

1. Generative probabilistic models extend the scope of inferential structure determination

   Olsson, Simon, Boomsma, Wouter, Frellsen, Jes, Bottaro, Sandro, Harder, Tim, Ferkinghoff-Borg, Jesper, and Hamelryck, Thomas

   Journal of Magnetic Resonance 2011

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