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The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of High-Performance Computers (HPC) systems.
Shirts, M.; Pande, V. S. COMPUTING: Screen Savers of the World Unite! Science 2000
1903
Earlier work this paper cites.
Koopman, B. O. Hamiltonian systems and transformation in Hilbert space. Proc. Natl. Acad. Sci. U.S.A. 1931
1931
Earlier work this paper cites.
Schütte, C.; Fischer, A.; Huisinga, W.; Deuflhard, P. A Direct Approach to Conformational Dynamics Based on Hybrid Monte Carlo. J. Comput. Phys. 1999
1999
Earlier work this paper cites.
Singhal, N.; Pande, V. S. Error analysis and efficient sampling in Markovian state models for molecular dynamics. J. Chem. Phys. 2005
2005
Earlier work this paper cites.
Coifman, R. R.; Lafon, S.; Lee, A. B.; Maggioni, M.; Nadler, B.; Warner, F.; Zucker, S. W. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proc. Natl. Acad. Sci. U.S.A. 2005
2005
Earlier work this paper cites.
Peters, B.; Trout, B. L. Obtaining reaction coordinates by likelihood maximization. J. Chem. Phys. 2006
2006
Earlier work this paper cites.
Buchete, N.-V.; Hummer, G. Coarse Master Equations for Peptide Folding Dynamics. J. Phys. Chem. B 2008
2008
Earlier work this paper cites.
Krivov, S. V.; Karplus, M. Diffusive reaction dynamics on invariant free energy profiles. Proc. Natl. Acad. Sci. U.S.A. 2008
2008
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.
Buch, I.; Harvey, M. J.; Giorgino, T.; Anderson, D. P.; De Fabritiis, G. High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing. J. Chem. Inf. Model. 2010
2010
Earlier work this paper cites.
Bowman, G. R.; Ensign, D. L.; Pande, V. S. Enhanced modeling via network theory: adaptive sampling of Markov state models. J. Chem. Theory Comput. 2010
2010
Earlier work this paper cites.
Weber, J. K.; Pande, V. S. Characterization and rapid sampling of protein folding Markov state model topologies. J. Chem. Theory Comput. 2011
2011
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.
Rohrdanz, M. A.; Zheng, W.; Maggioni, M.; Clementi, C. Determination of reaction coordinates via locally scaled diffusion map. J. Chem. Phys. 2011
2011
Earlier work this paper cites.
Zheng, W.; Qi, B.; Rohrdanz, M. A.; Caflisch, A.; Dinner, A. R.; Clementi, C. Delineation of Folding Pathways of a β \beta -Sheet Miniprotein. J. Phys. Chem. B 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.
Piana, S.; Lindorff-Larsen, K.; E. Shaw, D. How Robust Are Protein Folding Simulations with Respect to Force Field. Biophys. J. 2011
2011
Earlier work this paper cites.
Beauchamp, K. A.; McGibbon, R.; Lin, Y.-S.; S. Pande, V. Simple few-state models reveal hidden complexity in protein folding. Proc. Natl. Acad. Sci. USA 2012
2012
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.
Noé, F.; Nüske, F. A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. Multiscale Model. Sim. 2013
2013
Earlier work this paper cites.
Röblitz, S.; Weber, M. Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification. Adv. Data Anal. Classif. 2013
2013
Earlier work this paper cites.
others,, et al. Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. SC’14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2014; pp 41–53
2014
Earlier work this paper cites.
Preto, J.; Clementi, C. Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics. Phys. Chem. Chem. Phys. 2014
2014
Earlier work this paper cites.
Dickson, A.; Brooks, C. L. WExplore: Hierarchical Exploration of High-Dimensional Spaces Using the Weighted Ensemble Algorithm. J. Phys. Chem. B 2014
2014
Cited alongside, same era.
R. Bowman, G.; Pande, V.; Noé, F. Advances in Experimental Medicine and Biology ; Springer, 2014; Vol. 797
2014
Cited alongside, same era.
Nüske, F.; Keller, B. G.; Pérez-Hernández, G.; Mey, A. S. J. S.; Noé, F. Variational Approach to Molecular Kinetics. J. Chem. Theory Comput. 2014
2014
Cited alongside, same era.
Harada, R.; Kitao, A. Nontargeted Parallel Cascade Selection Molecular Dynamics for Enhancing the Conformational Sampling of Proteins. J. Chem. Theory Comput. 2015
2015
Cited alongside, same era.
Zimmerman, M. I.; Bowman, G. R. FAST Conformational Searches by Balancing Exploration/Exploitation Trade-Offs. J. Chem. Theory Comput. 2015
2015
Eastman, P.; Swails, J.; Chodera, J. D.; McGibbon, R. T.; Zhao, Y.; Beauchamp, K. A.; Wang, L.-P.; Simmonett, A. C.; Harrigan, M. P.; Stern, C. D.; Wiewiora, R. P.; Brooks, B. R.; Pande, V. S. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLoS Comput. Biol. 2017
2017
Later among the works it cites.
Hruska, E.; Abella, J. R.; Nüske, F.; Kavraki, L. E.; Clementi, C. Quantitative comparison of adaptive sampling methods for protein dynamics. J. Chem. Phys. 2018
2018
Later among the works it cites.
Guo, A. Z.; Sevgen, E.; Sidky, H.; Whitmer, J. K.; Hubbell, J. A.; J. de Pablo, J. Adaptive enhanced sampling by force-biasing using neural networks. J. Chem. Phys. 2018
2018
Later among the works it cites.
Zimmerman, M. I.; Porter, J. R.; Sun, X.; R. Silva, R.; Bowman, G. R. Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes. J. Chem. Theory Comput. 2018
2018
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Cited alongside, same era.
Boninsegna, L.; Gobbo, G.; Noé, F.; Clementi, C. Investigating Molecular Kinetics by Variationally Optimized Diffusion Maps. J. Chem. Theory Comput. 2015
2015
Cited alongside, same era.
Williams, M. O.; Rowley, C. W.; Kevrekidis, I. G. A kernel-based method for data-driven Koopman spectral analysis. J. Comput. Phys. 2015
2015
Cited alongside, same era.
Williams, M. O.; Kevrekidis, I. G.; Rowley, C. W. A data–driven approximation of the koopman operator: Extending dynamic mode decomposition. J. Nonlinear Sci. 2015
2015
Cited alongside, same era.
Noé, F.; Clementi, C. Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation. J. Chem. Theory Comput. 2015
2015
Cited alongside, same era.
Scherer, M. K.; Trendelkamp-Schroer, B.; Paul, F.; Pérez-Hernàndez, G.; Hoffmann, M.; Plattner, N.; Wehmeyer, C.; Prinz, J.-H.; Noé, F. PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J. Chem. Theory Comput. 2015
2015
Cited alongside, same era.
Doerr, S.; Harvey, M.; Noé, F.; De Fabritiis, G. HTMD: high-throughput molecular dynamics for molecular discovery. J. Chem. Theory Comput. 2016
2016
Cited alongside, same era.
Trendelkamp-Schroer, B.; Noé, F. Efficient Estimation of Rare-Event Kinetics. Phys. Rev. X 2016
2016
Cited alongside, same era.
Later among the works it cites.
Sidky, H.; Colón, Y. J.; Helfferich, J.; Sikora, B. J.; Bezik, C.; Chu, W.; Giberti, F.; Guo, A. Z.; Jiang, X.; Lequieu, J.; Li, J.; Moller, J.; Quevillon, M. J.; Rahimi, M.; Ramezani-Dakhel, H.; Rathee, V. S.; Reid, D. R.; Sevgen, E.; Thapar, V.; Webb, M. A.; Whitmer, J. K.; de Pablo, J. J. SSAGES: Software Suite for Advanced General Ensemble Simulations. J. Chem. Phys. 2018
2018
Later among the works it cites.
Ribeiro, J. M. L.; Bravo, P.; Wang, Y.; Tiwary, P. Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE). J. Chem. Phys. 2018
2018
Later among the works it cites.
Husic, B. E.; Pande, V. S. Markov State Models: From an Art to a Science. J. Am. Chem. Soc. 2018
2018
Later among the works it cites.
Mardt, A.; Pasquali, L.; Wu, H.; Noé, F. VAMPnets for deep learning of molecular kinetics. Nat. Commun. 2018
2018
Later among the works it cites.
Wehmeyer, C.; Noé, F. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. J. Chem. Phys. 2018
2018
Later among the works it cites.
Ribeiro, J. M. L.; Bravo, P.; Wang, Y.; Tiwary, P. Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE). J. Chem. Phys. 2018
2018
Later among the works it cites.
Turilli, M.; Merzky, A.; Balasubramanian, V.; Jha, S. Building blocks for workflow system middleware. 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 2018; pp 348–349
2018
Later among the works it cites.
Balasubramanian, V.; Turilli, M.; Hu, W.; Lefebvre, M.; Lei, W.; Modrak, R.; Cervone, G.; Tromp, J.; Jha, S. Harnessing the power of many: Extensible toolkit for scalable ensemble applications. 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 2018; pp 536–545
2018
Later among the works it cites.
Merzky, A.; Turilli, M.; Maldonado, M.; Santcroos, M.; Jha, S. Using Pilot Systems to Execute Many Task Workloads on Supercomputers. Workshop on Job Scheduling Strategies for Parallel Processing. 2018; pp 61–82
2018
Later among the works it cites.
Turilli, M.; Santcroos, M.; Jha, S. A comprehensive perspective on pilot-job systems. ACM Computing Surveys (CSUR) 2018
2018
Later among the works it cites.
Shkurti, A.; Styliari, I. D.; Balasubramanian, V.; Bethune, I.; Pedebos, C.; Jha, S.; Laughton, C. A. CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space. J. Chem. Theory Comput. 2019
2019
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2019
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2019
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2019
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2019
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2019
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2019
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2020
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2069
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