Fetching the paper…
Reading the bibliography…
Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations.
Zwanzig, R. W. High-temperature equation of state by a perturbation method. I. Nonpolar gases. Journal of Chemical Physics 1954
1954
Earlier work this paper cites.
Tobias, D. J.; Brooks III, C. L. Conformational equilibrium in the alanine dipeptide in the gas phase and aqueous solution: A comparison of theoretical results. J. Phys. Chem. 1992
1992
Earlier work this paper cites.
Lyubartsev, A. P.; Laaksonen, A. Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach. Phys. Rev. E 1995
1995
Earlier work this paper cites.
Clementi, C.; Nymeyer, H.; Onuchic, J. N. Topological and energetic factors: what determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? An investigation for small globular proteins. J. Mol. Biol. 2000
2000
Earlier work this paper cites.
Reith, D.; Pütz, M.; Müller-Plathe, F. Deriving effective mesoscale potentials from atomistic simulations. Journal of Computational Chemistry 2003
2003
Earlier work this paper cites.
Clementi, C.; Garcıa, A. E.; Onuchic, J. N. Interplay Among Tertiary Contacts, Secondary Structure Formation and Side-chain Packing in the Protein Folding Mechanism: All-atom Representation Study of Protein L. J. Mol. Biol. 2003
2003
Earlier work this paper cites.
Matysiak, S.; Clementi, C. Optimal combination of theory and experiment for the characterization of the protein folding landscape of S6: How far can a minimalist model go? J. Mol. Biol. 2004
2004
Earlier work this paper cites.
Das, P.; Matysiak, S.; Clementi, C. Balancing energy and entropy: A minimalist model for the characterization of protein folding landscapes. Proc. Natl. Acad. Sci. USA 2005
2005
Earlier work this paper cites.
Izvekov, S.; Voth, G. A. A Multiscale Coarse-Graining Method for Biomolecular Systems. J. Phys. Chem. B 2005
2005
Earlier work this paper cites.
Matysiak, S.; Clementi, C. Minimalist Protein Model as a Diagnostic Tool for Misfolding and Aggregation. J. Mol. Biol. 2006
2006
Earlier work this paper cites.
LeCun, Y.; Chopra, S.; Hadsell, R.; Ranzato, M.; Huang, F. Predicting Structured Data ; The MIT Press, 2007
2007
Earlier work this paper cites.
Clementi, C. Coarse-grained models of protein folding: Toy-models or predictive tools? Curr. Opin. Struct. Biol. 2008
2008
Earlier work this paper cites.
Noid, W. G.; Chu, J.-W.; Ayton, G. S.; Krishna, V.; Izvekov, S.; Voth, G. A.; Das, A.; Andersen, H. C. The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models. J. Chem. Phys. 2008
2008
Earlier work this paper cites.
Shell, M. S. The relative entropy is fundamental to multiscale and inverse thermodynamic problems. J. Chem. Phys. 2008
2008
Earlier work this paper cites.
Ciccotti, G.; Lelievre, T.; Vanden-Eijnden, E. Projection of diffusions on submanifolds: Application to mean force computation. Commun. Pure Appl. Math. 2008
2008
Earlier work this paper cites.
Mullinax, J. W.; Noid, W. G. Generalized Yvon-Born-Green Theory for Molecular Systems. Phys. Rev. Lett. 2009
2009
Earlier work this paper cites.
Schwantes, C. R.; Pande, V. S. Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J. Chem. Theory Comput. 2013
2009
Earlier work this paper cites.
Schwantes, C. R.; Pande, V. S. Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J. Chem. Theory Comput. 2013
2009
Earlier work this paper cites.
Tabak, E. G.; Vanden-Eijnden, E., et al. Density estimation by dual ascent of the log-likelihood. Communications in Mathematical Sciences 2010
2010
Earlier work this paper cites.
Prinz, J.-H.; Wu, H.; Sarich, M.; Keller, B.; Senne, M.; Held, M.; Chodera, J. D.; Schütte, C.; Noé, F. Markov models of molecular kinetics: Generation and validation. J. Chem. Phys. 2011
2011
Earlier work this paper cites.
Lindorff-Larsen, K.; Piana, S.; Dror, R. O.; Shaw, D. E. How fast-folding proteins fold. Science 2011
2011
Earlier work this paper cites.
Naritomi, Y.; Fuchigami, S. Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions. The Journal of Chemical Physics 2011
2011
Earlier work this paper cites.
Lindorff-Larsen, K.; Piana, S.; Dror, R. O.; Shaw, D. E. How fast-folding proteins fold. Science 2011
2011
Earlier work this paper cites.
Naritomi, Y.; Fuchigami, S. Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions. The Journal of Chemical Physics 2011
2011
Earlier work this paper cites.
Saunders, M. G.; Voth, G. A. Coarse-Graining Methods for Computational Biology. Annu. Rev. Bioph. Biom. 2013
2013
Earlier work this paper cites.
Noid, W. G. Perspective: Coarse-grained models for biomolecular systems. J. Chem. Phys. 2013
2013
Earlier work this paper cites.
Pérez-Hernández, G.; Paul, F.; Giorgino, T.; De Fabritiis, G.; Noé, F. Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 2013
2013
Earlier work this paper cites.
Pérez-Hernández, G.; Paul, F.; Giorgino, T.; De Fabritiis, G.; Noé, F. Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 2013
2013
Earlier work this paper cites.
Ingólfsson, H. I.; Lopez, C. A.; Uusitalo, J. J.; de Jong, D. H.; Gopal, S. M.; Periole, X.; Marrink, S. J. The power of coarse graining in biomolecular simulations. WIREs Comput. Mol. Sci. 2014
2014
Earlier work this paper cites.
Kingma, D. P.; Welling, M. Auto-Encoding Variational Bayes. 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings. 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Douc, R.; Moulines, E.; Stoffer, D. Nonlinear time series: Theory, methods and applications with R examples ; CRC press, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Shaw, D. E.; Grossman, J. P.; Bank, J. A.; Batson, B.; Butts, J. A.; Chao, J. C.; Deneroff, M. M.; Dror, R. O.; Even, A.; Fenton, C. H.; Forte, A.; Gagliardo, J.; Gill, G.; Greskamp, B.; Ho, C. R.; Ierardi, D. J.; Iserovich, L.; Kuskin, J. S.; Larson, R. H.; Layman, T.; Lee, L. S.; Lerer, A. K.; Li, C.; Killebrew, D.; Mackenzie, K. M.; Mok, S. Y. H.; Moraes, M. A.; Mueller, R.; Nociolo, L. J.; Peticolas, J. L.; Quan, T.; Ramot, D.; Salmon, J. K.; Scarpazza, D. P.; Ben Schafer, U.; Siddique, N.; Snyder, C. W.; Spengler, J.; Tang, P. T. P.; Theobald, M.; Toma, H.; Towles, B.; Vitale, B.; Wang, S. C.; Young, C. Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer. Int. Conf. High Perform. Comput. Networking, Storage Anal. SC 2014
2015
Cited alongside, same era.
Rezende, D.; Mohamed, S. Variational inference with normalizing flows. International Conference on Machine Learning. 2015; pp 1530–1538
2015
Cited alongside, same era.
Kalligiannaki, E.; Harmandaris, V.; Katsoulakis, M. A.; Plecháč, P. The geometry of generalized force matching and related information metrics in coarse-graining of molecular systems. J. Chem. Phys. 2015
Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N. E.; De Fabritiis, G.; Noé, F.; Clementi, C. Machine learning of coarse-grained molecular dynamics force fields. ACS central science 2019
2019
Later among the works it cites.
Nüske, F.; Boninsegna, L.; Clementi, C. Coarse-graining molecular systems by spectral matching. J. Chem. Phys. 2019
2019
Later among the works it cites.
Wang, J.; Chmiela, S.; Müller, K.-R.; Noé, F.; Clementi, C. Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. J. Chem. Phys. 2020
2020
Later among the works it cites.
Husic, B. E.; Charron, N. E.; Lemm, D.; Wang, J.; Pérez, A.; Majewski, M.; Krämer, A.; Chen, Y.; Olsson, S.; de Fabritiis, G., et al. Coarse graining molecular dynamics with graph neural networks. J. Chem. Phys. 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
Schrödinger, LLC, The PyMOL Molecular Graphics System, Version 1.8. 2015
2015
Cited alongside, same era.
Kmiecik, S.; Gront, D.; Kolinski, M.; Wieteska, L.; Dawid, A. E.; Kolinski, A. Coarse-grained protein models and their applications. Chem. Rev. 2016
2016
Cited alongside, same era.
Wagner, J. W.; Dama, J. F.; Durumeric, A. E.; Voth, G. A. On the representability problem and the physical meaning of coarse-grained models. J. Chem. Phys. 2016
2016
Cited alongside, same era.
Dunn, N. J.; Foley, T. T.; Noid, W. G. Van der Waals perspective on coarse-graining: Progress toward solving representability and transferability problems. Acc. Chem. Res. 2016
2016
Cited alongside, same era.
Davtyan, A.; Voth, G. A.; Andersen, H. C. Dynamic force matching: Construction of dynamic coarse-grained models with realistic short time dynamics and accurate long time dynamics. J. Chem. Phys. 2016
2016
Cited alongside, same era.
Harmandaris, V.; Kalligiannaki, E.; Katsoulakis, M.; Plecháč, P. Path-space variational inference for non-equilibrium coarse-grained systems. J. Comput. Phys. 2016
2016
Cited alongside, same era.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016; pp 770–778
2016
Cited alongside, same era.
Plattner, N.; Doerr, S.; De Fabritiis, G.; Noé, F. Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat. Chem. 2017 910 2017
2017
Cited alongside, same era.
Smith, J. S.; Isayev, O.; Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 2017
2017
Cited alongside, same era.
2020
Later among the works it cites.
Nicoli, K. A.; Nakajima, S.; Strodthoff, N.; Samek, W.; Müller, K. R.; Kessel, P. Asymptotically unbiased estimation of physical observables with neural samplers. Phys. Rev. E 2020
2020
Later among the works it cites.
Wirnsberger, P.; Ballard, A.; Papamakarios, G.; Abercrombie, S.; Racanière, S.; Pritzel, A.; Blundell, C., et al. Targeted free energy estimation via learned mappings. The Journal of Chemical Physics 2020
2020
Later among the works it cites.
Stieffenhofer, M.; Wand, M.; Bereau, T. Adversarial reverse mapping of equilibrated condensed-phase molecular structures. Mach. Learn. Sci. Technol. 2020
2020
Later among the works it cites.
Wu, H.; Köhler, J.; Noe, F. Stochastic Normalizing Flows. Advances in Neural Information Processing Systems. 2020; pp 5933–5944
2020
Later among the works it cites.
2020
Later among the works it cites.
Chen, J.; Lu, C.; Chenli, B.; Zhu, J.; Tian, T. Vflow: More expressive generative flows with variational data augmentation. International Conference on Machine Learning. 2020; pp 1660–1669
2020
Later among the works it cites.
Cornish, R.; Caterini, A.; Deligiannidis, G.; Doucet, A. Relaxing bijectivity constraints with continuously indexed normalising flows. International Conference on Machine Learning. 2020; pp 2133–2143
2020
Later among the works it cites.
Nielsen, D.; Jaini, P.; Hoogeboom, E.; Winther, O.; Welling, M. Survae flows: Surjections to bridge the gap between vaes and flows. Advances in Neural Information Processing Systems. 2020
2020
Later among the works it cites.
Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing for Molecular Graphs. International Conference on Learning Representations (ICLR). 2020
2020
Later among the works it cites.
Husic, B. E.; Charron, N. E.; Lemm, D.; Wang, J.; Pérez, A.; Majewski, M.; Krämer, A.; Chen, Y.; Olsson, S.; de Fabritiis, G., et al. Coarse graining molecular dynamics with graph neural networks. J. Chem. Phys. 2020
2020
Later among the works it cites.
Rezende, D. J.; Papamakarios, G.; Racaniere, S.; Albergo, M.; Kanwar, G.; Shanahan, P.; Cranmer, K. Normalizing flows on tori and spheres. International Conference on Machine Learning. 2020; pp 8083–8092
2020
Later among the works it cites.
Wirnsberger, P.; Ballard, A.; Papamakarios, G.; Abercrombie, S.; Racanière, S.; Pritzel, A.; Blundell, C., et al. Targeted free energy estimation via learned mappings. The Journal of Chemical Physics 2020
2020
Later among the works it cites.
Shaw, D. E.; Adams, P. J.; Azaria, A.; Bank, J. A.; Batson, B.; Bell, A.; Bergdorf, M.; Bhatt, J.; Adam Butts, J.; Correi, T.; Dirks, R. M.; Dror, R. O.; Eastwoo, M. P.; Edwards, B.; Even, A.; Feldmann, P.; Fenn, M.; Fenton, C. H.; Forte, A.; Gagliardo, J.; Gill, G.; Gorlatova, M.; Greskamp, B.; Grossman, J. P.; Gullingsrud, J.; Harper, A.; Hasenplaugh, W.; Heily, M.; Heshmat, B. C.; Hunt, J.; Ierardi, D. J.; Iserovich, L.; Jackson, B. L.; Johnson, N. P.; Kirk, M. M.; Klepeis, J. L.; Kuskin, J. S.; Mackenzie, K. M.; Mader, R. J.; McGowen, R.; McLaughlin, A.; Moraes, M. A.; Nasr, M. H.; Nociolo, L. J.; O’Donnell, L.; Parker, A.; Peticolas, J. L.; Pocina, G.; Predescu, C.; Quan, T.; Salmon, J. K.; Schwink, C.; Shim, K. S.; Siddique, N.; Spengler, J.; Szalay, T.; Tabladillo, R.; Tartler, R.; Taube, A. G.; Theobald, M.; Towles, B.; Vick, W.; Wang, S. C.; Wazlowski, M.; Weingarten, M. J.; Williams, J. M.; Yuh, K. A. Anton 3: Twenty Microseconds of Molecular Dynamics Simulation before Lunch. Int. Conf. High Perform. Comput. Networking, Storage Anal. SC 2021
2021
Later among the works it cites.
Papamakarios, G.; Nalisnick, E.; Rezende, D. J.; Mohamed, S.; Lakshminarayanan, B. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research 2021
2021
Later among the works it cites.
Liu, Q.; Xu, J.; Jiang, R.; Wong, W. H. Density estimation using deep generative neural networks. Proc. Natl. Acad. Sci. U. S. A. 2021
2021
Later among the works it cites.
Ding, X.; Zhang, B. DeepBAR: A Fast and Exact Method for Binding Free Energy Computation. J. Phys. Chem. Lett. 2021
2021
Later among the works it cites.
Brofos, J.; Brubaker, M. A.; Lederman, R. R. Manifold Density Estimation via Generalized Dequantization. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models. 2021
2021
Later among the works it cites.
Köhler, J.; Krämer, A.; Noé, F. Smooth Normalizing Flows. Advances in Neural Information Processing Systems. 2021
2021
Later among the works it cites.
Kovács, D. P.; Oord, C. V. D.; Kucera, J.; Allen, A. E.; Cole, D. J.; Ortner, C.; Csányi, G. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE. J. Chem. Theory Comput. 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Chen, Y.; Krämer, A.; Charron, N. E.; Husic, B. E.; Clementi, C.; Noé, F. Machine learning implicit solvation for molecular dynamics. J. Chem. Phys. 2021
2021
Later among the works it cites.
Köhler, J.; Krämer, A.; Noé, F. Smooth Normalizing Flows. Advances in Neural Information Processing Systems. 2021
2021
Later among the works it cites.
Jin, J.; Pak, A. J.; Durumeric, A. E.; Loose, T. D.; Voth, G. A. Bottom-up Coarse-Graining: Principles and Perspectives. Journal of Chemical Theory and Computation 2022
2022
Closest in time.
Gabriéu, M.; Rotskoff, G. M.; Vanden-Eijnden, E. Adaptive Monte Carlo augmented with normalizing flows. Proceedings of the National Academy of Sciences of the United States of America 2022
2022
Closest in time.
Wang, W.; Xu, M.; Cai, C.; Miller, B. K.; Smidt, T. E.; Wang, Y.; Tang, J.; Gómez-Bombarelli, R. Generative Coarse-Graining of Molecular Conformations. International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. 2022; pp 23213–23236
2022
Closest in time.
Hoffmann, M.; Scherer, M.; Hempel, T.; Mardt, A.; de Silva, B.; Husic, B. E.; Klus, S.; Wu, H.; Kutz, N.; Brunton, S. L.; Noé, F. Deeptime: a Python library for machine learning dynamical models from time series data. Machine Learning: Science and Technology 2022
2022
Closest in time.
Pak, A. J.; Dannenhoffer-Lafage, T.; Madsen, J. J.; Voth, G. A. Systematic Coarse-Grained Lipid Force Fields with Semiexplicit Solvation via Virtual Sites. J. Chem. Theory Comput. 2019
2087
Closest in time.