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Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions and properties.
Protein folding and unfolding in microseconds to nanoseconds by experiment and simulation
Ugo Mayor, Christopher M Johnson, Valerie Daggett, and Alan R Fersht · 2000
Earlier work this paper cites.
Diffusing and colliding: the atomic level folding/unfolding pathway of a small helical protein
Mari L DeMarco, Darwin OV Alonso, and Valerie Daggett · 2004
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How fast-folding proteins fold
Kresten Lindorff-Larsen, Stefano Piana, Ron O Dror, and David E Shaw · 2011
Earlier work this paper cites.
Identification of slow molecular order parameters for markov model construction
Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé · 2013
Earlier work this paper cites.
Coarse-grained protein models and their applications
Sebastian Kmiecik, Dominik Gront, Michal Kolinski, Lukasz Wieteska, Aleksandra Elzbieta Dawid, and Andrzej Kolinski · 2016
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Markov state models: From an art to a science
Brooke E Husic and Vijay S Pande · 2018
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
Cited alongside, same era.
Building normalizing flows with stochastic interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
Cited alongside, same era.
Improving de novo protein binder design with deep learning
Nathaniel R Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, et al · 2023
Riemannian flow matching on general geometries
Ricky TQ Chen and Yaron Lipman · 2023
Later among the works it cites.
Timewarp: Transferable acceleration of molecular dynamics by learning time-coarsened dynamics
Leon Klein, Andrew YK Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, and Ryota Tomioka · 2023
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Machine learning coarse-grained potentials of protein thermodynamics
Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E Charron, Toni Giorgino, Brooke E Husic, Cecilia Clementi, Frank Noé, and Gianni De Fabritiis · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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Cited alongside, same era.
Fast protein backbone generation with se (3) flow matching
Jason Yim, Andrew Campbell, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Regina Barzilay, Tommi Jaakkola, et al
Cited in the paper.
Se (3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola
Cited in the paper.
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, and Ricky TQ Chen · 2023
Later among the works it cites.