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Efficient sampling from the Boltzmann distribution given its energy function is a key challenge for modeling complex physical systems such as molecules.
Long-timescale molecular dynamics simulations of protein structure and function
John L Klepeis, Kresten Lindorff-Larsen, Ron O Dror, and David E Shaw · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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The future of molecular dynamics simulations in drug discovery
David W Borhani and David E Shaw · 2012
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
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Notes on noise contrastive estimation and negative sampling. arxiv
C Dyer · 2014
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Markov state models of biomolecular conformational dynamics
John D Chodera and Frank Noé · 2014
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Molecular dynamics
Benedict Leimkuhler · 2015
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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Gfn2-xtb—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Christoph Bannwarth, Sebastian Ehlert, and Stefan Grimme · 2019
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Improved contrastive divergence training of energy based models
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2020
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Generalized energy based models
Michael Arbel, Liang Zhou, and Arthur Gretton · 2020
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Telescoping density-ratio estimation
Benjamin Rhodes, Kai Xu, and Michael U Gutmann · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Targeted free energy estimation via learned mappings
Peter Wirnsberger, Andrew J Ballard, George Papamakarios, Stuart Abercrombie, Sébastien Racanière, Alexander Pritzel, Danilo Jimenez Rezende, and Charles Blundell · 2020
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror · 2020
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Scipy 1.0: fundamental algorithms for scientific computing in python
Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al · 2020
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Smooth normalizing flows
Jonas Köhler, Andreas Krämer, and Frank Noé · 2021
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How to train your energy-based models
Yang Song and Diederik P Kingma · 2021
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Analyzing and improving the optimization landscape of noise-contrastive estimation
Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2021
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Deepbar: a fast and exact method for binding free energy computation
Xinqiang Ding and Bin Zhang · 2021
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Estimation of thermodynamic observables in lattice field theories with deep generative models
Kim A Nicoli, Christopher J Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, and Paolo Stornati · 2021
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Equivariant graph neural networks for 3d macromolecular structure
Bowen Jing, Stephan Eismann, Pratham N Soni, and Ron O Dror · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
Equivariant flow matching with hybrid probability transport for 3d molecule generation
Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, and Wei-Ying Ma · 2023
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Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E Charron, Felix Musil, Andrea Guljas, Yaoyi Chen, Klara Bonneau, Aldo S Pasos-Trejo, Jacopo Venturin, Daria Gusew, Iryna Zaporozhets, Andreas Krämer, et al · 2023
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Flow-matching: Efficient coarse-graining of molecular dynamics without forces
Jonas Kohler, Yaoyi Chen, Andreas Kramer, Cecilia Clementi, and Frank Noé · 2023
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Guiding energy-based models via contrastive latent variables
Hankook Lee, Jongheon Jeong, Sejun Park, and Jinwoo Shin · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
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Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
Cited alongside, same era.
Deeptime: a python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven L Brunton, et al · 2021
Cited alongside, same era.
"hey, that’s not an ode": Faster ode adjoints via seminorms
Patrick Kidger, Ricky T. Q. Chen, and Terry J. Lyons · 2021
Cited alongside, same era.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al · 2022
Cited alongside, same era.
Temperature steerable flows and boltzmann generators
Manuel Dibak, Leon Klein, Andreas Krämer, and Frank Noé · 2022
Cited alongside, same era.
Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2022
Cited alongside, same era.
Pitfalls of gaussians as a noise distribution in nce
Holden Lee, Chirag Pabbaraju, Anish Sevekari, and Andrej Risteski · 2022
Cited alongside, same era.
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl · 2023
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Graphformers: Gnn-nested transformers for representation learning on textual graph, 2023
Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie · 2023
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Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, and Yoshua Bengio · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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Transferable boltzmann generators
Leon Klein and Frank Noé · 2024
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Boltzmann generators and the new frontier of computational sampling in many-body systems
Alessandro Coretti, Sebastian Falkner, Jan Weinreich, Christoph Dellago, and O Anatole von Lilienfeld · 2024
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
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Alphafold meets flow matching for generating protein ensembles
Bowen Jing, Bonnie Berger, and Tommi Jaakkola · 2024
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Scalable normalizing flows enable boltzmann generators for macromolecules
Joseph C Kim, David Bloore, Karan Kapoor, Jun Feng, Ming-Hong Hao, and Mengdi Wang · 2024
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Efficient mapping of phase diagrams with conditional boltzmann generators
Maximilian Schebek, Michele Invernizzi, Frank Noé, and Jutta Rogal · 2024
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Predicting equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, et al · 2024
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Protein conformation generation via force-guided se (3) diffusion models
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, and Quanquan Gu · 2024
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Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics
Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, and Simon Olsson · 2024
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Accelerating nce convergence with adaptive normalizing constant computation
Anish Sevekari, Rishal Aggarwal, Maria Chikina, and David Koes · 2024
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Classification diffusion models: Revitalizing density ratio estimation
Shahar Yadin, Noam Elata, and Tomer Michaeli · 2024
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Bnem: A boltzmann sampler based on bootstrapped noised energy matching
RuiKang OuYang, Bo Qiang, Zixing Song, and José Miguel Hernández-Lobato · 2024
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Alphafold3 in drug discovery: A comprehensive assessment of capabilities, limitations, and applications
Haiyang Zheng and Jin Wang · 2025
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Scalable equilibrium sampling with sequential boltzmann generators
Charlie B. Tan, Avishek Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, and Alexander Tong · 2025
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Scalable emulation of protein equilibrium ensembles with generative deep learning
Sarah Lewis, Tim Hempel, José Jiménez-Luna, Michael Gastegger, Yu Xie, Andrew YK Foong, Victor García Satorras, Osama Abdin, Bastiaan S Veeling, Iryna Zaporozhets, et al · 2025
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