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Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles.
Equivariant flows: sampling configurations for multi-body systems with symmetric energies
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The Amber biomolecular simulation programs
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Activation mechanism of the β \beta 2-adrenergic receptor
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How fast-folding proteins fold
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How does a drug molecule find its target binding site?
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Score-based generative modeling through stochastic differential equations
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Computer simulation in chemical physics , volume 397
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Adam: A method for stochastic optimization
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Application of molecular-dynamics based markov state models to functional proteins
Malmstrom, R. D.; Lee, C. T.; Van Wart, A. T.; and Amaro, R. E. 2014 · 2014
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Perspective: Markov models for long-timescale biomolecular dynamics
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Gaussian accelerated molecular dynamics: unconstrained enhanced sampling and free energy calculation
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Accelerated molecular dynamics simulations of protein folding
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Role of molecular dynamics and related methods in drug discovery
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Graded activation and free energy landscapes of a muscarinic G-protein–coupled receptor
Pytorch: An imperative style, high-performance deep learning library
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Applied stochastic differential equations , volume 10
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Denoising diffusion probabilistic models
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Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
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Equivariant flows: exact likelihood generative learning for symmetric densities
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Miao, Y.; and McCammon, J. A. 2016 · 2016
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Bypassing the Kohn-Sham equations with machine learning
Brockherde, F.; Vogt, L.; Li, L.; Tuckerman, M. E.; Burke, K.; and Müller, K.-R. 2017 · 2017
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Machine learning of accurate energy-conserving molecular force fields
Chmiela, S.; Tkatchenko, A.; Sauceda, H. E.; Poltavsky, I.; Schütt, K. T.; and Müller, K.-R. 2017 · 2017
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Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
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Deep potential: A general representation of a many-body potential energy surface
Han, J.; Zhang, L.; Car, R.; et al. 2017 · 2017
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Gaussian accelerated molecular dynamics: Theory, implementation, and applications
Miao, Y.; and McCammon, J. A. 2017 · 2017
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Gaussian accelerated molecular dynamics in NAMD
Pang, Y. T.; Miao, Y.; Wang, Y.; and McCammon, J. A. 2017 · 2017
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Köhler, J.; Klein, L.; and Noé, F. 2020 · 2020
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Enhanced sampling and free energy calculations for protein simulations
Liao, Q. 2020 · 2020
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Replica-exchange Nos \ \backslash ’e-Hoover dynamics for Bayesian learning on large datasets
Luo, R.; Zhang, Q.; Yang, Y.; and Wang, J. 2020 · 2020
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Ligand Gaussian accelerated molecular dynamics (LiGaMD): Characterization of ligand binding thermodynamics and kinetics
Miao, Y.; Bhattarai, A.; and Wang, J. 2020 · 2020
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Machine learning for molecular simulation
Noé, F.; Tkatchenko, A.; Müller, K.-R.; and Clementi, C. 2020 · 2020
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Efficient learning of generative models via finite-difference score matching
Pang, T.; Xu, K.; Li, C.; Song, Y.; Ermon, S.; and Zhu, J. 2020 · 2020
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Gemnet: Universal directional graph neural networks for molecules
Klicpera, J.; Becker, F.; and Günnemann, S. 2021 · 2021
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Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
Park, C. W.; Kornbluth, M.; Vandermause, J.; Wolverton, C.; Kozinsky, B.; and Mailoa, J. P. 2021 · 2021
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E (n) equivariant graph neural networks
Satorras, V. G.; Hoogeboom, E.; and Welling, M. 2021 · 2021
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Learning gradient fields for molecular conformation generation
Shi, C.; Luo, S.; Xu, M.; and Tang, J. 2021 · 2021
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Gaussian accelerated molecular dynamics: Principles and applications
Wang, J.; Arantes, P. R.; Bhattarai, A.; Hsu, R. V.; Pawnikar, S.; Huang, Y.-m. M.; Palermo, G.; and Miao, Y. 2021 · 2021
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3D-Transformer: Molecular Representation with Transformer in 3D Space
Wu, F.; Zhang, Q.; Radev, D.; Cui, J.; Zhang, W.; Xing, H.; Zhang, N.; and Chen, H. 2021 · 2021
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Self-consistent determination of long-range electrostatics in neural network potentials
Gao, A.; and Remsing, R. C. 2022 · 2022
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Equivariant Graph Mechanics Networks with Constraints
Huang, W.; Han, J.; Rong, Y.; Xu, T.; Sun, F.; and Huang, J. 2022 · 2022
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GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
Xu, M.; Yu, L.; Song, Y.; Shi, C.; Ermon, S.; and Tang, J. 2022 · 2022
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