Intrinsic map dynamics exploration for uncharted effective free-energy landscapes
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Significance: Direct simulations explore the dynamics of physical systems at their natural pace. Molecular dynamics (MD) simulations (e.g., of macromolecular folding) extensively revisit typical configurations until rare and interesting transition events occur. Biasing the simulator away from regions already explored can, therefore, drastically accelerate the discovery of features. We propose an enhanced sampling simulation framework, where MD and machine learning adaptively bootstrap each other. Machine learning guides the search for important configurations by processing information from previous explorations. This search proceeds iteratively in an algorithmically orchestrated fashion without advance knowledge of suitable collective variables. Applied to a molecular sensor of lipid saturation in membranes, a helix dissociation pathway not seen in millisecond simulations is discovered at the second iteration.
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The challenge of identifying the most appropriate low-dimensional space to accelerate conformational sampling is reflected in the fact that most early (and recent) applications of dimensionality reduction to atomistic modeling involved simulations of rare events or phase transitions.
“The challenge of identifying the most appropriate low-dimensional space to accelerate conformational sampling is reflected in the fact that most of early (and recent) applications of dimensionality reduction to atomistic modeling involved simulations of rare events or phase transitions 71,72,95-98 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [98]
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VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
Molecular dynamics with coupling to an external bath
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,626 · see citing works
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015) · SoftwareX · doi:10.1016/j.softx.2015.06.001 · Cited by 27,190 · see citing works
GROMACS: Fast, flexible, and free
van der Spoel, D., Lindahl, E. R., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20291 · Cited by 19,292 · see citing works
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,024 · see citing works
GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. R. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700301q · Cited by 15,934 · see citing works
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. P. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 10,444 · see citing works
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., et al. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt055 · Cited by 7,493 · see citing works
The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations
Marrink, . J., Risselada, H. J., Yefimov, S., Tieleman, D. P., & de Vries, A. H. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp071097f · Cited by 5,527 · see citing works
MDAnalysis: A toolkit for the analysis of molecular dynamics simulations
Michaud‐Agrawal, N., Denning, E. J., Woolf, T. B., & Beckstein, O. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21787 · Cited by 4,549 · see citing works
Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., Yang, R., Cieplak, P., Luo, R., Lee, T.-S., et al. (2003) · Journal of Computational Chemistry · doi:10.1002/jcc.10349 · Cited by 4,271 · see citing works
IPython: A System for Interactive Scientific Computing
Pérez, F., & Granger, B. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.53 · Cited by 3,928 · see citing works
Crystal Structure and Pair Potentials: A Molecular-Dynamics Study
Parrinello, M., & Rahman, A. (1980) · Physical Review Letters · doi:10.1103/physrevlett.45.1196 · Cited by 3,782 · see citing works
A solution for the best rotation to relate two sets of vectors
Kabsch, W. (1976) · Acta Crystallographica Section A · doi:10.1107/s0567739476001873 · Cited by 3,462 · see citing works
PLUMED 2: New feathers for an old bird
Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C., & Bussi, G. (2013) · Computer Physics Communications · doi:10.1016/j.cpc.2013.09.018 · Cited by 3,427 · see citing works
The MARTINI Coarse-Grained Force Field: Extension to Proteins
Monticelli, L., Kandasamy, S. K., Périole, X., Larson, R. G., Tieleman, D. P., & Marrink, . J. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700324x · Cited by 2,491 · see citing works
Three-dimensional alpha shapes
Edelsbrunner, H., & Mücke, E. P. (1994) · ACM Transactions on Graphics · doi:10.1145/174462.156635 · Cited by 2,488 · see citing works
On the shape of a set of points in the plane
Edelsbrunner, H., Kirkpatrick, D. G., & Seidel, R. (1983) · IEEE Transactions on Information Theory · doi:10.1109/tit.1983.1056714 · Cited by 1,904 · see citing works
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675 · see citing works
Self-citationRencher, A. C. (2002) · Wiley series in probability and statistics · doi:10.1002/0471271357.ch12 · Cited by 1,666 · see citing works
AUTO-07p: Continuation and bifurcation software for ordinary differential equations
Doedel, E. J., CHAMPNEYS, A. R., Dercole, F., Fairgrieve, T. F., Kuznetsov, Y. A., Oldeman, B. E., Paffenroth, R. C., Sandstede, B., Wang, X. J., & Zhang, C. H. (2007) · Cited by 1,631 · see citing works
Wassenaar, T. A., Ingólfsson, H. I., Böckmann, R. A., Tieleman, D. P., & Marrink, S. J. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00209 · Cited by 1,174 · see citing works
A discussion of the solution for the best rotation to relate two sets of vectors
Kabsch, W. (1978) · Acta Crystallographica Section A · doi:10.1107/s0567739478001680 · Cited by 1,151 · see citing works
Using collective variables to drive molecular dynamics simulations
Fiorin, G., Klein, M. L., & Hénin, J. (2013) · Molecular Physics · doi:10.1080/00268976.2013.813594 · Cited by 1,053 · see citing works
Gear, C. W., Hyman, J. M., Kevrekidid, P. G., Kevrekidis, I. G., Runborg, O., & Theodoropoulos, C. (2003) · Communications in Mathematical Sciences · doi:10.4310/cms.2003.v1.n4.a5 · Cited by 778 · see citing works
Self-citationTransition-path theory and path-finding algorithms for the study of rare events.
E, W., & Vanden-Eijnden, E. (2010) · Annual review of physical chemistry (Print) · doi:10.1146/annurev.physchem.040808.090412 · Cited by 682 · see citing works
Maragliano, L., & Vanden‐Eijnden, E. (2006) · Chemical Physics Letters · doi:10.1016/j.cplett.2006.05.062 · Cited by 521 · see citing works
Abrams, C. F., & Bussi, G. (2013) · Entropy · doi:10.3390/e16010163 · Cited by 429 · see citing works
Reaction coordinates of biomolecular isomerization
Bolhuis, P. G., Dellago, C., & Chandler, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.100127697 · Cited by 426 · see citing works
Equation‐free: The computer‐aided analysis of complex multiscale systems
Kevrekidis, I. G., Gear, C. W., & Hummer, G. (2004) · AIChE Journal · doi:10.1002/aic.10106 · Cited by 365 · see citing works
Self-citationAn efficient library for parallel ray tracing and animation
Stone, J. E. (1998) · Cited by 307 · see citing works
Zachmann, H. G. (1975) · Zeitschrift für Physikalische Chemie · doi:10.1524/zpch.1975.95.4-6.320 · Cited by 303 · see citing works
Determination of reaction coordinates via locally scaled diffusion map
Rohrdanz, M. A., Zheng, W., Maggioni, M., & Clementi, C. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3569857 · Cited by 292 · see citing works
Dankowicz, H., & Schilder, F. (2013) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611972573 · Cited by 272 · see citing works
Rohrdanz, M. A., Zheng, W., & Clementi, C. (2013) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-040412-110006 · Cited by 242 · see citing works
Hummer, G., & Kevrekidis, I. G. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1574777 · Cited by 235 · see citing works
Self-citationAbrams, J. B., & Tuckerman, M. E. (2008) · The Journal of Physical Chemistry B · doi:10.1021/jp805039u · Cited by 221 · see citing works
Automatic Lip-Contour Extraction and Mouth-Structure Segmentation in Images
Gómez-Mendoza, J.-B., Prieto, F., & Redarce, T. (2010) · Computing in Science & Engineering · doi:10.1109/mcse.2010.145 · Cited by 187 · see citing works
Reaction Coordinates and Mechanistic Hypothesis Tests.
Peters, B. (2016) · Annual review of physical chemistry (Print) · doi:10.1146/annurev-physchem-040215-112215 · Cited by 184 · see citing works
A self-learning algorithm for biased molecular dynamics
Tribello, G. A., Ceriotti, M., & Parrinello, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011511107 · Cited by 166 · see citing works
Transition Path Sampling and Other Advanced Simulation Techniques for Rare Events
Dellago, C., & Bolhuis, P. G. (2009) · Springer eBooks · doi:10.1007/978-3-540-87706-6_3 · Cited by 159 · see citing works
Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
Singer, A., Radek Erban, A., Kevrekidis, I. G., & Coifman, R. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905547106 · Cited by 151 · see citing works
Self-citationData-driven parameterization of the generalized Langevin equation
Lei, H., Baker, N., & Li, X. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1609587113 · Cited by 146 · see citing works
A Eukaryotic Sensor for Membrane Lipid Saturation
Covino, R., Ballweg, S., Stordeur, C., Michaelis, J. B., Puth, K., Wernig, F., Bahrami, A. H., Ernst, A. M., Hummer, G., & Ernst, R. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.05.015 · Cited by 139 · see citing works
Self-citationLelimousin, M. G., Limongelli, V., & Sansom, M. S. P. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b05602 · Cited by 127 · see citing works
Milestoning without a Reaction Coordinate
Májek, P., & Elber, R. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct100114j · Cited by 112 · see citing works
Surface Reconstruction by Wrapping Finite Sets in Space
Edelsbrunner, H. (2003) · Algorithms and combinatorics · doi:10.1007/978-3-642-55566-4_17 · Cited by 111 · see citing works
Enhanced sampling techniques in biomolecular simulations
Spiwok, V., Sucur, Z., & Hosek, P. (2014) · Biotechnology Advances · doi:10.1016/j.biotechadv.2014.11.011 · Cited by 110 · see citing works
BORDER: efficient computation of boundary points
Xia, C., Hsu, W., Lee, M. L., & Ooi, B. C. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.38 · Cited by 109 · see citing works
Parameter Estimation for Multiscale Diffusions
Pavliotis, G. A., & Stuart, A. M. (2007) · Journal of Statistical Physics · doi:10.1007/s10955-007-9300-6 · Cited by 95 · see citing works
An Algorithm for Piecewise-Linear Approximation of an Implicitly Defined Manifold
Allgower, E. L., & Schmidt, P. H. (1985) · SIAM Journal on Numerical Analysis · doi:10.1137/0722020 · Cited by 89 · see citing works
Parameter estimation for multiscale diffusionsPreprint
Pavliotis, G. A. (2012) · Cited by 88 · see citing works
Rapid exploration of configuration space with diffusion-map-directed molecular dynamics
Zheng, W., Rohrdanz, M. A., & Clementi, C. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp401911h · Cited by 86 · see citing works
Locating landmarks on high-dimensional free energy surfaces
Chen, M., Yu, T.-Q., & Tuckerman, M. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418241112 · Cited by 84 · see citing works
Dominant folding pathways of a WW domain
Beccara, S. A., Škrbić, T., Covino, R., & Faccioli, P. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111796109 · Cited by 78 · see citing works
Self-citationReduced Models in Chemical Kinetics via Nonlinear Data-Mining
Chiavazzo, E., Gear, C. W., Dsilva, C. J., Rabin, N., & Kevrekidis, I. G. (2014) · Processes · doi:10.3390/pr2010112 · Cited by 57 · see citing works
Self-citationLi, J., Kevrekidis, P. G., Gear, C. W., & Kevrekidis, I. G. (2007) · SIAM Review · doi:10.1137/070692303 · Cited by 47 · see citing works
Self-citationNonlinear intrinsic variables and state reconstruction in multiscale simulations
Dsilva, C. J., Talmon, R., Rabin, N., Coifman, R. R., & Kevrekidis, I. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4828457 · Cited by 39 · see citing works
Self-citationSonday, B. E., Haataja, M., & Kevrekidis, I. G. (2009) · Physical Review E · doi:10.1103/physreve.80.031102 · Cited by 39 · see citing works
Self-citationData-driven coarse graining in action: Modeling and prediction of complex systems
Krumscheid, S., Pradas, M. M., Pavliotis, G. A., & Kalliadasis, S. (2015) · Physical Review E · doi:10.1103/physreve.92.042139 · Cited by 36 · see citing works
Adaptive simplification of complex multiscale systems
Chiavazzo, E., & Karlin, I. V. (2011) · Physical Review E · doi:10.1103/physreve.83.036706 · Cited by 28 · see citing works
Self-citationHeat Shock Factor 1: From Fire Chief to Crowd-Control Specialist
Triandafillou, C. G., & Drummond, D. A. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.06.026 · Cited by 24 · see citing works
Chiavazzo, E. (2011) · Journal of Computational Physics · doi:10.1016/j.jcp.2011.11.007 · Cited by 23 · see citing works
Self-citationA new framework for extracting coarse-grained models from time series with multiscale structure
Kalliadasis, S., Krumscheid, S., & Pavliotis, G. A. (2015) · Journal of Computational Physics · doi:10.1016/j.jcp.2015.05.002 · Cited by 20 · see citing works
Calderon, C. P. (2007) · Multiscale Modeling and Simulation · doi:10.1137/050643647 · Cited by 20 · see citing works
Coarse Collective Dynamics of Animal Groups
Frewen, T. A., Couzin, I. D., Kolpas, A., Moehlis, J., Coifman, R. R., & Kevrekidis, I. G. (2010) · Lecture notes in computational science and engineering · doi:10.1007/978-3-642-14941-2_16 · Cited by 16 · see citing works
Self-citationExploration of effective potential landscapes using coarse reverse integration
Frewen, T. A., Hummer, G., & Kevrekidis, I. G. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3207882 · Cited by 15 · see citing works
Self-citationLearning the Geometric Structure of Manifolds with Singularities Using the Tensor Voting Graph
Deutsch, S., & Medioni, G. G. (2016) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-016-0684-2 · Cited by 12 · see citing works
Belkin, M., Que, Q., Wang, Y., & Zhou, X. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.6727 · Cited by 5 · see citing works
Manifolds defined by points: Parameterizing and boundary detection (extended abstract)
Gear, C. W., Chiavazzo, E., & Kevrekidis, I. G. (2016) · AIP conference proceedings · doi:10.1063/1.4951749 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
2 of the 73 references couldn’t be matched to a known paper.
Research works printed but not linked: 34
- [1][1] C. Abrams and G. Bussi, Entropy 16 , 163 (2013), arXiv:arXiv:1401.0387v1. - · arXiv
- [3][3] W. E and E. Vanden-Eijnden, Annual review of physical chemistry 61 , 391 (2010). -
- [7][7] H. Lei, N. Baker, and X. Li, Proceedings of the National Academy of Sciences 113 , 14183 (2016). -
- [8][8] R. Coifman, S. Lafon, A. Lee, B. Nadler, F. Warner, and S. Zucker, Proceedings of the National Academy of Sciences 102 , 7426 (2005). -
- [10][10] T. Frewen, G. Hummer, and I. Kevrekidis, The Journal of Chemical Physics 131 , 134104 (2009). -
- [13][13] W. Zheng, M. Rohrdanz, and C. Clementi, The Journal of Physical Chemistry B 117 , 12769 (2013). -
- [14][14] G. Hummer and I. Kevrekidis, The Journal of Chemical Physics 118 , 10762 (2003). -
- [15][15] Y. e. a. Duan, Journal of Computational Chemistry 24 , 1999 (2003). -
- [17][17] S. Marrink, H. Risselada, S. Yefimov, D. Tieleman, and A. De Vries, Journal of Physical Chemistry B 111 , 7812 (2007). -
- [18][18] L. Monticelli, S. Kandasamy, X. Periole, R. Larson, D. Tieleman, and S. Marrink, Journal of Chemical Theory and Computation 4 , 819 (2008). -
- [19][19] H. Edelsbrunner, D. Kirkpatrick, and R. Seidel, IEEE Transactions on Information Theory 29 , 551 (1983). -
- [20][20] H. Edelsbrunner and P. Mucke, ACM Transactions on Graphics (TOG) 13 , 43 (1994). -
- [22][22] S. Lafon, Diffusion Maps and Geometric Harmonics , Ph.D. thesis, Yale University (2004). -
- [25][25] C. Dsilva, R. Talmon, N. Rabin, R. Coifman, and I. G. Kevrekidis, The Journal of Chemical Physics 139 , 184109 (2013). -
- [26][26] M. Lelimousin, V. Limongelli, and M. S. P. Sansom, Journal of the American Chemical Society 138 , 10611 (2016). -
- [27][27] E. Chiavazzo and I. Karlin, Physical Review E 83 , 036706 (2011). -
- [28][28] I. Kevrekidis, C. Gear, and G. Hummer, AIChe Journal 50 , 1346 (2004). -
- [30][30] C. Dsilva, Manifold Learning for Dynamical Systems (PhD thesis, Princeton University, Princeton NJ, 2015). -
- [31][31] A. Singer, R. Erban, I. Kevrekidis, and R. Coifman, Proceedings of the National Academy of Sciences 106 , 16090 (2009). -
- [32][32] G. Pavliotis and A. Stuart, Journal of Statistical Physics 127 , 741 (2007). -
- [33][33] S. Kalliadasis, S. Krumscheid, and G. Pavliotis, Journal of Computational Physics 296 , 314 (2015). -
- [34][34] S. Krumscheid, M. Pradas, G. Pavliotis, and S. Kalliadasis, Physical Review E 92 , 042139 (2015). -
- [38][38] S. Deutsch and G. Medioni, Journal of Mathematical Imaging and Vision , 1 (2016). -
- [39][39] M. Rohrdanz, W. Zheng, M. Maggioni, and C. Clementi, The Journal of Chemical Physics 134 , 124116 (2011). -
- [40][40] B. Peters, Annual Review of Physical Chemistry 67 , 669 (2016). -
- [44][44] B. Hess, C. Kutzner, D. van der Spoel, and E. Lindahl, Journal of Chemical Theory and Communication 4 , 435 (2008). -
- [45][45] D. van der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. Mark, and H. Berendsen, Journal of Computational Chemistry 26 , 1701 (2005). -
- [46][46] G. Bussi, D. Donadio, and M. Parrinello, Journal of Chemical Physics 126 , 014101 (2007). -
- [49][49] T. Wassenaar, H. Ingólfsson, R. Böckmann, D. Tieleman, and S. Marrink, Journal of Chemical Theory and Computation 11 , 2144 (2015). -
- [50][50] H. Berendsen, J. Postma, W. van Gunsteren, A. DiNola, and J. Haak, The Journal of Chemical Physics 81 , 3684 (1984). -
- [51][51] M. Parrinello and A. Rahman, Physical Review Letters 45 , 1196 (1980). -
- [58][58] N. Michaud-Agrawal, E. Denning, T. Woolf, and O. Beckstein, Journal of Computational Chemistry 32 , 2319 (2011). -
- [59][59] W. Humphrey, A. Dalke, and K. Schulten, Journal of Molecular Graphics 14 , 33 (1996). -
- [60][60] J. Stone, In Intel Supercomputer Users Group Proceedings , 1 (1995).
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Sidky, H., Chen, W., & Ferguson, A. L. (2020) · Molecular Physics · doi:10.1080/00268976.2020.1737742 · Cited by 147 · see citing works
Machine-guided path sampling to discover mechanisms of molecular self-organization
Jung, H., Covino, R., Arjun, A., Leitold, C., Christoph Dellago, A., Bolhuis, P. G., & Hummer, G. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00428-z · Cited by 144 · see citing works
Self-citationStochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces
Schneider, E., Dai, L., Topper, R. Q., Drechsel‐Grau, C., & Tuckerman, M. E. (2017) · Physical Review Letters · doi:10.1103/physrevlett.119.150601 · Cited by 139 · see citing works
Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space
Degiacomi, M. T. (2018) · Structure · doi:10.1016/j.str.2019.03.018 · Cited by 127 · see citing works
“Generative neural networks have been recently proposed as a tool for the discovery of collective variables, useful to extract kinetic information from molecular simulations or to guide the sampling of poorly explored regions (Chen et al., 2018; Chiavazzo et al., 2017; Hernández et al., 2018;Mardt et al., 2018; Ribeiro…”
Shamsi, Z., Cheng, K. J., & Shukla, D. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b06521 · Cited by 123 · see citing works
Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations
Rogal, J., Schneider, E., & Tuckerman, M. E. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.245701 · Cited by 102 · see citing works
Inverse methods for design of soft materials
Sherman, Z. M., Howard, M. P., Lindquist, B. A., Jadrich, R. B., & Truskett, T. M. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5145177 · Cited by 100 · see citing works
Harpole, T. J., & Delemotte, L. (2017) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.10.033 · Cited by 94 · see citing works
Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76 · see citing works
Ribeiro, J. M. L., & Tiwary, P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00869 · Cited by 65 · see citing works
Ravindra, P., Smith, Z., & Tiwary, P. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00115h · Cited by 67 · see citing works
Computational methods for exploring protein conformations
Allison, J. R. (2020) · Biochemical Society Transactions · doi:10.1042/bst20200193 · Cited by 54 · see citing works
Mancardi, G., Mikołajczyk, A., Annapoorani, V. K., Bahl, A., Blekos, K., Burk, J., Çetin, Y. A., Chairetakis, K., Dutta, S., Escorihuela, L., et al. (2023) · Materials Today · doi:10.1016/j.mattod.2023.05.029 · Cited by 60 · see citing works
Self-citationTransition Manifolds of Complex Metastable Systems
Bittracher, A., Koltai, P., Klus, S., Banisch, R., Dellnitz, M., & Schütte, C. (2017) · Journal of nonlinear science · doi:10.1007/s00332-017-9415-0 · Cited by 46 · see citing works
Molecular dynamics based enhanced sampling of collective variables with very large time steps
Chen, P.-Y., & Tuckerman, M. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.4999447 · Cited by 55 · see citing works
Molecular latent space simulators
Sidky, H., Chen, W., & Ferguson, A. L. (2020) · Chemical Science · doi:10.1039/d0sc03635h · Cited by 52 · see citing works
Rational design of patchy colloids via landscape engineering
Long, A., & Ferguson, A. L. (2017) · Molecular Systems Design & Engineering · doi:10.1039/c7me00077d · Cited by 48 · see citing works
Balancing Force Field Protein–Lipid Interactions To Capture Transmembrane Helix–Helix Association
Domański, J., Sansom, M. S. P., Stansfeld, P. J., & Best, R. B. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00983 · Cited by 47 · see citing works
Frontiers in Multiscale Modeling of Photoreceptor Proteins
Mroginski, M. A., Adam, S., Amoyal, G. S., Barnoy, A., Bondar, A., Borin, V. A., Church, J. R., Domratcheva, T. M., Ensing, B., Fanelli, F., et al. (2020) · Photochemistry and Photobiology · doi:10.1111/php.13372 · Cited by 47 · see citing works
“dependent component analysis, slow-mode separation, autoencoder-based dimensionality reduction and many more techniques (126-130).”
MethodDiscovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks
Hooft, F., Ortíz, A. P. D. A., & Ensing, B. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00981 · Cited by 50 · see citing works
Manifold learning for parameter reduction
Holiday, A., Kooshkbaghi, M., Bello‐Rivas, J. M., Gear, C. W., Zagaris, A., & Kevrekidis, I. G. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2019.04.015 · Cited by 45 · see citing works
“the biasing of computational experiments in [23,24]).”
Self-citationLinking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations
Arbabi, H., Bunder, J. E., Samaey, G., Roberts, A. J., & Kevrekidis, I. G. (2020) · JOM · doi:10.1007/s11837-020-04399-8 · Cited by 45 · see citing works
Self-citationData-Driven Molecular Dynamics: A Multifaceted Challenge
Bernetti, M., Bertazzo, M., & Masetti, M. (2020) · Pharmaceuticals · doi:10.3390/ph13090253 · Cited by 43 · see citing works
Landmark diffusion maps (L-dMaps): Accelerated manifold learning out-of-sample extension
Long, A. W., & Ferguson, A. L. (2017) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2017.08.004 · Cited by 40 · see citing works
Cuendet, M. A., Margul, D. T., Schneider, E., Vogt-Maranto, L., & Tuckerman, M. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5027479 · Cited by 37 · see citing works
Nonlinear machine learning in simulations of soft and biological materials
Wang, J., & Ferguson, A. L. (2017) · Molecular Simulation · doi:10.1080/08927022.2017.1400164 · Cited by 35 · see citing works
“iMapD is a recently developed enhanced sampling technique reported by Kevrekidis and co-workers in 2017 [128].”
MethodTrezza, G., Bergamasco, L., Fasano, M., & Chiavazzo, E. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00806-7 · Cited by 35 · see citing works
Self-citationLocal and global perspectives on diffusion maps in the analysis of molecular systems
Trstanova, Z., Leimkuhler, B., & Tony Lelièvre, A. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0036 · Cited by 34 · see citing works
Dynamical coring of Markov state models.
Nagel, D., Weber, A., Lickert, B., & Stock, G. (2019) · Journal of Chemical Physics · doi:10.1063/1.5081767 · Cited by 29 · see citing works
Smith, Z., Ravindra, P., Wang, Y., Cooley, R., & Tiwary, P. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c03985 · Cited by 31 · see citing works
Recruiting machine learning methods for molecular simulations of proteins
Mittal, S., & Shukla, D. (2018) · Molecular Simulation · doi:10.1080/08927022.2018.1448976 · Cited by 31 · see citing works
“Similar ideas are used by methods such as iMapD [102], SGOOP [103], ExtendedDM-d-MD [104] and AES [105] to achieve enhanced sampling efficiency.”
MethodRadhakrishnan, R. (2020) · AIChE Journal · doi:10.1002/aic.17026 · Cited by 28 · see citing works
“More recently, ML approaches have proved useful in learning high-dimensional free energy surfaces, 191,192 and in providing a low dimensional set of CVs. 193 Some examples are discussed below.”
BackgroundKibria, M. R., Akbar, R., Nidadavolu, P., Havryliuk, O., Lafond, S., & Azimi, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-27729-7 · Cited by 26 · see citing works
Shmilovich, K., Stieffenhofer, M., Charron, N., & Hoffmann, M. (2022) · Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c07716 · Cited by 32 · see citing works
Shmilovich, K., & Ferguson, A. L. (2023) · Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c00505 · Cited by 34 · see citing works
Falciani, G., & Chiavazzo, E. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117366 · Cited by 25 · see citing works
“To overcome this impasse, we believe that new advances in data mining and deep learning to significantly accelerate atomistic models can play a critical role in the near future [203].”
Self-citationDouble Diffusion Maps and their Latent Harmonics for scientific computations in latent space
Evangelou, N., Dietrich, F., Chiavazzo, E., Lehmberg, D., Meilă, M., & Kevrekidis, I. G. (2023) · Journal of Computational Physics · doi:10.1016/j.jcp.2023.112072 · Cited by 24 · see citing works
“In that direction further research might needed in order to utilize effective sampling algorithms combined with algorithms that aim to learn operators on the data [5].”
BackgroundSelf-citationLemke, T., Berg, A., Jain, A., & Peter, C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00675 · Cited by 25 · see citing works
Wang, J., Gayatri, M., & Ferguson, A. L. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b01634 · Cited by 23 · see citing works
Contour forward flux sampling: Sampling rare events along multiple collective variables
DeFever, R. S., & Sarupria, S. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5063358 · Cited by 23 · see citing works
Desgranges, C., & Delhommelle, J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/d0me00134a · Cited by 22 · see citing works
Spectral Map: Embedding Slow Kinetics in Collective Variables
Rydzewski, J. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c01101 · Cited by 21 · see citing works
Dominant Reaction Pathways by Quantum Computing
Hauke, P., Mattiotti, G., & Faccioli, P. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.028104 · Cited by 20 · see citing works
Machine learning of slow collective variables and enhanced sampling via spatial techniques
Gökdemir, T., & Rydzewski, J. (2025) · Chemical Physics Reviews · doi:10.1063/5.0245177 · Cited by 20 · see citing works
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
Rydzewski, J., Chen, M., Ghosh, T. K., & Valsson, Ó. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00873 · Cited by 20 · see citing works
Fast conformational clustering of extensive molecular dynamics simulation data
Hunkler, S., Diederichs, K., Kukharenko, O., & Peter, C. (2023) · Journal of Chemical Physics · doi:10.1063/5.0142797 · Cited by 16 · see citing works
Manifold learning in atomistic simulations: a conceptual review
Rydzewski, J., Chen, M., & Valsson, Ó. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ace81a · Cited by 21 · see citing works
Hunkler, S., Lemke, T., Peter, C., & Kukharenko, O. (2019) · Journal of Chemical Physics · doi:10.1063/1.5115398 · Cited by 17 · see citing works
Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features
She, Z., Ge, P., & Lei, H. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0130033 · Cited by 18 · see citing works
Learning dynamical information from static protein and sequencing data
Pearce, P., Woodhouse, F. G., Forrow, A., Kelly, A., Kusumaatmaja, H., & Dunkel, J. (2019) · Nature Communications · doi:10.1038/s41467-019-13307-x · Cited by 14 · see citing works
“Although much progress has been made in dimensionality reduction 25 – 27 and the reconstruction of effective energy landscapes in these settings 3,13,16,17,28 , the problem of inferring dynamical information such as protein-folding or mutation pathways and rates from instantaneous ensemble data…”
BackgroundA Koopman framework for rare event simulation in stochastic differential equations
Zhang, B. J., Sahai, T., & Marzouk, Y. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111025 · Cited by 17 · see citing works
“Our approach can also be related to the work of [43], in which the authors combine trajectory data from molecular dynamics simulations with nonlinear manifold learning techniques to inform the exploration of rare regions of state space.”
BackgroundSampling rare conformational transitions with a quantum computer
Ghamari, D., Hauke, P., Covino, R., & Faccioli, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-20032-x · Cited by 17 · see citing works
“1: First, ML and MD perform a preliminary uncharted exploration of the most visited regions of the configuration space [32].”
BackgroundMethodInfluentialSelf-citationToward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics
Zou, Z., Tsai, S.-T., & Tiwary, P. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c07595 · Cited by 16 · see citing works
CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting
Damjanovic, J., Murphy, J. M., & Lin, Y.-S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00598 · Cited by 17 · see citing works
Evangelou, N., Wichrowski, N. J., Kevrekidis, G. A., Dietrich, F., Kooshkbaghi, M., McFann, S. E., & Kevrekidis, I. G. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac154 · Cited by 14 · see citing works
“A discussion of the systematic collection of additional data, parsimoniously extending the known “patches” of the level sets is discussed in [9].”
BackgroundSelf-citationCharitidis, C. A., Koumoulos, E. P., Dragatogiannis, D. A., & Unlatched, K. (2020) · De Gruyter eBooks · doi:10.1515/9783110479133 · Cited by 14 · see citing works
Cappabianca, R., Angelis, P. D., Cardellini, A., Chiavazzo, E., & Asinari, P. (2022) · ACS Omega · doi:10.1021/acsomega.2c05218 · Cited by 12 · see citing works
Self-citationUnderstanding ML Driven HPC: Applications and Infrastructure
Jha, S., & Fox, G. (2019) · International Conference on e-Science · doi:10.1109/escience.2019.00054 · Cited by 12 · see citing works
“As simulations proceed the model learns the structure or even underlying principles, and is gradually refined either by coarse-graining or using better approximations to the effective potentials [36].”
An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients
Georgiou, A., Bello‐Rivas, J. M., Gear, C. W., Wu, H., Chiavazzo, E., & Kevrekidis, I. G. (2017) · Entropy · doi:10.3390/e19070294 · Cited by 10 · see citing works
“It is worth stressing that, as opposed to our previous work [49], here, we use a non-linear scheme to lift into the ambient space the extended boundary points.”
Self-citationSpectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles
Rydzewski, J. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00428 · Cited by 9 · see citing works
Howard, R. J., Carnevale, V., Delemotte, L., Hellmich, U. A., & Rothberg, B. S. (2017) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.12.013 · Cited by 9 · see citing works
“Recent developments, which remain to be applied to ion transport systems, take advantage of machine learning techniques which allow identification of “slow” degrees of freedom in a data-driven manner [150, 151].”
BackgroundExtracting collective motions underlying nucleosome dynamics via nonlinear manifold learning
Guo, A. Z., Lequieu, J. P., & Pablo, J. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5063851 · Cited by 9 · see citing works
Sampling a Rare Protein Transition Using Quantum Annealing
Ghamari, D., Covino, R., & Faccioli, P. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01174 · Cited by 8 · see citing works
“…superposition overcomes the autocorrelation problem, the uncharted exploration implemented on the classical computer exploits a data-driven scheme [11] to enhance the probability of diffusing into previously unexplored regions, thus promoting the chance of discovering multiple transition channels.”
InfluentialSelf-citationSTIM1 transmembrane helix dimerization captured by AI-guided transition path sampling
Horvath, F., Jung, H., Grabmayr, H., Fahrner, M., Romanin, C., & Hummer, G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2506516122 · Cited by 9 · see citing works
“Previously, this process would typically be investigated in coarse-grained [31, 53–58], implicit-membrane [59, 60] or biased [55,56,61–65] simulations.”
Self-citationLearning Markovian dynamics with spectral maps.
Rydzewski, J., & Gökdemir, T. (2023) · Journal of Chemical Physics · doi:10.1063/5.0189241 · Cited by 8 · see citing works
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
Fox, G., & Jha, S. (2019) · eScience · doi:10.1109/escience.2019.00057 · Cited by 14 · see citing works
“Use of machine learning to guide molecular dynamics simulations to explore full range of phase space [29].”
BackgroundLaurent, H., Hughes, M. D. G., Walko, M., Brockwell, D. J., Mahmoudi, N., Youngs, T. G. A., Headen, T. F., & Dougan, L. (2023) · Biomacromolecules · doi:10.1021/acs.biomac.3c00583 · Cited by 7 · see citing works
Nijhawan, A. K., Chan, A., Hsu, D. J., Chen, L. X., & Kohlstedt, K. (2021) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c05820 · Cited by 6 · see citing works
3 Modeling carbon-based smart materials
Srivastava, R., Fasano, M., Nejad, S. M., Thielemann, H. C., Chiavazzo, E., & Asinari, P. (2020) · De Gruyter eBooks · doi:10.1515/9783110479133-003 · Cited by 7 · see citing works
“While sophisticated strategies for speeding up complex MD simulations of systems that undergo significant configuration changes have been recently suggested on the basis of the equation-free method [136, 137], coarse-grained (CG) models have been specifically developed to simulate polymers [138–140] and carbon fillers…”
MethodSelf-citationA Hybrid Hamiltonian for the Accelerated Sampling along Experimental Restraints
Peter, E. K., & Černý, J. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20020370 · Cited by 6 · see citing works
A beginner’s approach to deep learning applied to VS and MD techniques
Dhondt, S., Jose Oramas, M., & Winter, H. L. D. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00985-7 · Cited by 5 · see citing works
Characterising Intrinsically Disordered Proteins Using NMR Spectroscopy and MD Simulations
Heller, G. T., Yu, L., & Hansen, D. F. (2022) · Royal Society of Chemistry eBooks · doi:10.1039/9781839165702-00383 · Cited by 5 · see citing works
The curse of dimensionality loses its power
Tuckerman, M. E. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00182-0 · Cited by 5 · see citing works
Learning effective SDEs from Brownian dynamic simulations of colloidal particles
Evangelou, N., Dietrich, F., Bello‐Rivas, J. M., Yeh, A., Hendley, R. S., Bevan, M. A., & Kevrekidis, I. G. (2023) · Molecular Systems Design & Engineering · doi:10.1039/d2me00086e · Cited by 5 · see citing works
Self-citationData-Driven Methods in Multiscale Modeling of Soft Matter
Bereau, T. (2018) · Springer eBooks · doi:10.1007/978-3-319-42913-7_40-1 · Cited by 5 · see citing works
GANs and Closures: Micro-Macro Consistency in Multiscale Modeling
Crabtree, E., Bello‐Rivas, J. M., Ferguson, A. L., & Kevrekidis, I. G. (2023) · Multiscale Modeling and Simulation · doi:10.1137/22m1517834 · Cited by 5 · see citing works
Self-citationUnderstanding ML driven HPC: Applications and InfrastructurePreprint
Fox, G., & Jha, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.02363 · Cited by 5 · see citing works
“As simulations proceed the model learns the structure or even underlying principles, and is gradually refined either by coarse-graining or using better approximations to the effective potentials [36].”
Learning dynamical information from static protein and sequencing data
Pearce, P., Woodhouse, F. G., Forrow, A., Kelly, A., Kusumaatmaja, H., & Dunkel, J. (2018) · doi:10.1101/401067 · Cited by 4 · see citing works
Nonlinear Model Reduction for Slow–Fast Stochastic Systems Near Unknown Invariant Manifolds
Ye, F. X.-F., Yang, S., & Maggioni, M. (2023) · Journal of Nonlinear Science · doi:10.1007/s00332-023-09998-8 · Cited by 7 · see citing works
“This is a model for the butane molecule, inspired by molecular dynamics [LL12, SMK + 17], in the form of overdamped Langevin equations in R 6 (see appendix C.3).”
BackgroundMethodInfluentialJackel, E., Lazzeri, G., & Covino, R. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c05242 · Cited by 4 · see citing works
Self-citationAnalysis of dynamically stable patterns in a maze-like corridor using the Wasserstein metric
Ishiwata, R., Kinukawa, R., & Sugiyama, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24777-2 · Cited by 4 · see citing works
Cotter, S. L., Kevrekidis, I. G., & Russell, P. T. (2020) · SIAM/ASA Journal on Uncertainty Quantification · doi:10.1137/19m1239416 · Cited by 4 · see citing works
“In [15], these maps were used to propagate data-mining into unexplored regions which were predicted to be close to the lower-dimensional manifold.”
MethodSelf-citationAchieving Reversible Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVEPreprint
Ribeiro, J. M. L., & Tiwary, P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/400002 · Cited by 4 · see citing works
Evangelou, N., Cui, T., Bello‐Rivas, J. M., Makeev, A. G., & Kevrekidis, I. G. (2024) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0187511 · Cited by 3 · see citing works
Self-citationApplications of enhanced sampling methods to biomolecular self-assembly: a review
Hooten, M., Patel, H., Shao, Y., Singh, R. K., & Dutt, M. (2025) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ade650 · Cited by 3 · see citing works
“Some of the dMap-based enhanced sampling methods are diffusion-map-directed MD (DM-d-MD) (Rohrdanz et al 2011) and intrinsic map dynamics (iMapD) (Chiavazzo et al 2017).”
Computational Exploration of Conformational Transitions in Protein Drug Targets
Cossins, B. P., Lawson, A. D. G., & Shi, J. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_17 · Cited by 3 · see citing works
Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds
Georgiou, A., Vandecasteele, H., Bello‐Rivas, J. M., & Kevrekidis, I. G. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0178947 · Cited by 3 · see citing works
Self-citation
- Authors
- Chiavazzo, E., Covino, R., Coifman, R. R., Gear, C. W., Georgiou, A. S., Hummer, G., & Kevrekidis, I. G.
- Affiliations
- Goethe University Frankfurt (DE)
- Politecnico di Torino (IT)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-06-20
- Volume
- 114
- Issue
- 28
- Pages
- E5494-E5503
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5514710
- OpenAlex
- W2571642833
- Cited by
- 125
- References
- 73
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 5 · 2023: 17 · 2022: 14 · 2021: 11 · 2020: 23 · 2019: 18 · 2018: 16 · 2017: 9
- FWCI
- 7.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PORTO@Iris (submittedVersion, other-oa)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion, other-oa)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca (2012LHPSJC)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- enhanced sampling, free energy landscape, machine learning, molecular dynamics, adaptive biasing, collective variables, lipid saturation
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