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Coarse-grained models are a core computational tool in theoretical chemistry and biophysics.
The potential of mean force surface for the alanine dipeptide in aqueous solution: A theoretical approach
B. Montgomery Pettitt and Martin Karplus · 1985
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Conformational equilibrium in the alanine dipeptide in the gas phase and aqueous solution: A comparison of theoretical results
Douglas J. Tobias and Charles L. Brooks · 1992
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A Multiscale Coarse-Graining Method for Biomolecular Systems
Sergei Izvekov and Gregory A. Voth · 2005
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Multiscale coarse graining of liquid-state systems
Sergei Izvekov and Gregory A. Voth · 2005
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Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction
Payel Das, Mark Moll, Hernán Stamati, Lydia E. Kavraki, and Cecilia Clementi · 2006
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The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models
W. G. Noid, Jhih-Wei Chu, Gary S. Ayton, Vinod Krishna, Sergei Izvekov, Gregory A. Voth, Avisek Das, and Hans C. Andersen · 2008
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Coarse-grained models of protein folding: Toy models or predictive tools?
Cecilia Clementi · 2008
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Crystal Structure of a Ten-Amino Acid Protein
Shinya Honda, Toshihiko Akiba, Yusuke S. Kato, Yoshito Sawada, Masakazu Sekijima, Miyuki Ishimura, Ayako Ooishi, Hideki Watanabe, Takayuki Odahara, and Kazuaki Harata · 2008
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Projection of diffusions on submanifolds: Application to mean force computation
Giovanni Ciccotti, Tony Lelièvre, and Eric Vanden-Eijnden · 2008
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Density estimation by dual ascent of the log-likelihood
Esteban G. Tabak and Eric Vanden-Eijnden · 2010
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Coarse-graining of multiprotein assemblies
Marissa G Saunders and Gregory A Voth · 2012
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2013
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Identification of slow molecular order parameters for Markov model construction
Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé · 2013
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Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9
Christian R. Schwantes and Vijay S. Pande · 2013
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Time Step Rescaling Recovers Continuous-Time Dynamical Properties for Discrete-Time Langevin Integration of Nonequilibrium Systems
David A. Sivak, John D. Chodera, and Gavin E. Crooks · 2014
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Variational Inference with Normalizing Flows
Danilo Rezende and Shakir Mohamed · 2015
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Connecting Free Energy Surfaces in Implicit and Explicit Solvent: An Efficient Method To Compute Conformational and Solvation Free Energies
Nanjie Deng, Bin W. Zhang, and Ronald M. Levy · 2015
Cited alongside, same era.
The geometry of generalized force matching and related information metrics in coarse-graining of molecular systems
Evangelia Kalligiannaki, Vagelis Harmandaris, Markos A. Katsoulakis, and Petr Plecháč · 2015
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Breaking the Curse of Dimensionality with Convex Neural Networks
Francis Bach · 2017
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Modeling the mechanism of CLN025 beta-hairpin formation
Keri A. McKiernan, Brooke E. Husic, and Vijay S. Pande · 2017
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OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, Bernard R. Brooks, and Vijay S. Pande · 2017
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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Coarse graining molecular dynamics with graph neural networks
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, and Cecilia Clementi · 2020
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A multi-modal coarse grained model of DNA flexibility mappable to the atomistic level
Jürgen Walther, Pablo D Dans, Alexandra Balaceanu, Adam Hospital, Genís Bayarri, and Modesto Orozco · 2020
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Molecular latent space simulators
Hythem Sidky, Wei Chen, and Andrew L. Ferguson · 2020
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Smooth normalizing flows
Jonas Köhler, Andreas Krämer, and Frank Noe · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Cited alongside, same era.
Advances in coarse-grained modeling of macromolecular complexes
Alexander J Pak and Gregory A Voth · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization
Zachary Smith, Debabrata Pramanik, Sun-Ting Tsai, and Pratyush Tiwary · 2018
Cited alongside, same era.
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2018
Cited alongside, same era.
VAMPnets for deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu, and Frank Noé · 2018
Cited alongside, same era.
Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)
João Marcelo Lamim Ribeiro, Pablo Bravo, Yihang Wang, and Pratyush Tiwary · 2018
Cited alongside, same era.
SchNet – A deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
Cited alongside, same era.
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Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, and Eric Vanden-Eijnden · 2021
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Data-driven reaction coordinate discovery in overdamped and non-conservative systems: Application to optical matter structural isomerization
Shiqi Chen, Curtis W. Peterson, John A. Parker, Stuart A. Rice, Andrew L. Ferguson, and Norbert F. Scherer · 2021
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E(n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Generative coarse-graining of molecular conformations
Wujie Wang, Minkai Xu, Chen Cai, Benjamin K Miller, Tess Smidt, Yusu Wang, Jian Tang, and Rafael Gomez-Bombarelli · 2022
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From data to noise to data for mixing physics across temperatures with generative artificial intelligence
Yihang Wang, Lukas Herron, and Pratyush Tiwary · 2022
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Flow-matching – efficient coarse-graining molecular dynamics without forces
Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, and Frank Noé · 2022
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Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation
Amr H. Mahmoud, Matthew Masters, Soo Jung Lee, and Markus A. Lill · 2022
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Adaptive Monte Carlo augmented with normalizing flows
Marylou Gabrié, Grant M. Rotskoff, and Eric Vanden-Eijnden · 2022
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Trainability and Accuracy of Artificial Neural Networks: An Interacting Particle System Approach
Grant Rotskoff and Eric Vanden-Eijnden · 2022
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