The DOI matches its Crossref record; 71 of the 73 references it cites were found in a registry or an index.
Intrinsic map dynamics exploration for uncharted effective free-energy landscapes
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
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 31,057
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
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 19,461
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,327
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 15,830
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381
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., Smith, J., Kasson, P., van der Spoel, D., et al. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt055 · Cited by 7,255
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 5,641
Research works printed but not linked: 2
- S Lafon Diffusion maps and geometric harmonics. PhD thesis (Yale University New Haven CT). (2004).
- C Dsilva Manifold learning for dynamical systems. PhD thesis (Princeton University Princeton). (2015).
Cited by
Sorted by citations
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Machine-learned potentials for next-generation matter simulations
Friederich, P., Häse, F., Proppe, J., & Aspuru‐Guzik, A. (2021) · Nature Materials · doi:10.1038/s41563-020-0777-6 · Cited by 595
“Finally, machine learning models can also be used to enhance the sampling of MD simulations by learning bias potentials 147 and thus enable more efficient exploration of the configuration space based on data analysis 148 .”
MethodUnsupervised Learning Methods for Molecular Simulation Data
Glielmo, A., Husic, B. E., Rodríguez, Á., Clementi, C., Noé, F., & Laio, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01195 · Cited by 436
“Di ff usion map has been applied to analyze the slow transitions of molecules, 137,138 guide enhanced-sampling methods, 139 − 141 and have been combined with the kinetic variational principles described in section 6.”
MethodEnhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]
Hénin, J., Tony Lelièvre, A., Shirts, M. R., Valsson, Ó., & Delemotte, L. (2022) · Living Journal of Computational Molecular Science · doi:10.33011/livecoms.4.1.1583 · Cited by 460
“iMapD relies on clustering in a low-dimensional manifold inferred by dimensionality reduction and selecting states to seed from the boundaries of a diffusion map in diffusion coordinates [18] 9.”
BackgroundMethodMachine learning approaches for analyzing and enhancing molecular dynamics simulations
Wang, Y., Lamim Ribeiro, J. M., & Tiwary, P. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.016 · Cited by 303
“In the more recent iMapD approach[33], the boundary in a low-dimensional manifold comprising the top few eigenvectors of the diffusion map is constructed, and independent simulations are launched from points sampled from this boundary.”
BackgroundTwo decades of Martini: Better beads, broader scope
Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. T. (2023) · WIREs Computational Molecular Science · doi:10.1002/wcms.1620 · Cited by 255
Chen, W., & Ferguson, A. L. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25520 · Cited by 244
Gkeka, P., Stoltz, G., Farimani, A. B., Belkacemi, Z., Ceriotti, M., Chodera, J. D., Dinner, A. R., Ferguson, A. L., Maillet, J., Minoux, H., et al. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00355 · Cited by 222
Unsupervised machine learning in atomistic simulations, between predictions and understanding
Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5091842 · Cited by 173
Machine learning and data science in soft materials engineering
Ferguson, A. L. (2017) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aa98bd · Cited by 178
Perspective: Identification of collective variables and metastable states of protein dynamics
Sittel, F., & Stock, G. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5049637 · Cited by 149
Chen, W., Tan, A. R., & Ferguson, A. L. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023804 · Cited by 149
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Sidky, H., Chen, W., & Ferguson, A. L. (2020) · Molecular Physics · doi:10.1080/00268976.2020.1737742 · Cited by 157
Machine-guided path sampling to discover mechanisms of molecular self-organization
Jung, H., Covino, R., Arjun, A., Leitold, C., Dellago, C., Bolhuis, P. G., & Hummer, G. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00428-z · Cited by 158
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
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 130
“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 125
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 99
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 99
Harpole, T. J., & Delemotte, L. (2017) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.10.033 · Cited by 94
Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76
Ribeiro, J. M. L., & Tiwary, P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00869 · Cited by 70
Ravindra, P., Smith, Z., & Tiwary, P. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00115h · Cited by 67
Computational methods for exploring protein conformations
Allison, J. R. (2020) · Biochemical Society Transactions · doi:10.1042/bst20200193 · Cited by 68
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
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
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
Molecular latent space simulators
Sidky, H., Chen, W., & Ferguson, A. L. (2020) · Chemical Science · doi:10.1039/d0sc03635h · Cited by 57
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
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
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 48
“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 47
Manifold learning for parameter reduction
Holiday, A., Kooshkbaghi, M., Bello-Rivas, J., Gear, C., Zagaris, A., & Kevrekidis, I. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2019.04.015 · Cited by 50
“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
Self-citationData-Driven Molecular Dynamics: A Multifaceted Challenge
Bernetti, M., Bertazzo, M., & Masetti, M. (2020) · Pharmaceuticals · doi:10.3390/ph13090253 · Cited by 43
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
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
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
“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
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 35
Dynamical coring of Markov state models
Nagel, D., Weber, A., Lickert, B., & Stock, G. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5081767 · Cited by 33
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
Recruiting machine learning methods for molecular simulations of proteins
Mittal, S., & Shukla, D. (2018) · Molecular Simulation · doi:10.1080/08927022.2018.1448976 · Cited by 28
“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 29
“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
Shmilovich, K., Stieffenhofer, M., Charron, N. E., & Hoffmann, M. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c07716 · Cited by 27
Shmilovich, K., & Ferguson, A. L. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c00505 · Cited by 29
Falciani, G., & Chiavazzo, E. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117366 · Cited by 25
“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
“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
Wang, J., Gayatri, M., & Ferguson, A. L. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b01634 · Cited by 23
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
Desgranges, C., & Delhommelle, J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/d0me00134a · Cited by 19
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
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
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
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
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
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 24
Hunkler, S., Lemke, T., Peter, C., & Kukharenko, O. (2019) · Journal of Chemical Physics · doi:10.1063/1.5115398 · Cited by 17
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 20
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
“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
“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 18
“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
CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting
Damjanovic, J., Murphy, J. M., & Lin, Y. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00598 · Cited by 16
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
“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
Cappabianca, R., Angelis, P. D., Cardellini, A., Chiavazzo, E., & Asinari, P. (2022) · ACS Omega · doi:10.1021/acsomega.2c05218 · Cited by 12
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 16
“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
“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
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
“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
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
“…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
“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. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0189241 · Cited by 9
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
Fox, G., & Jha, S. (2019) · International Conference on e-Science · doi:10.1109/escience.2019.00057 · Cited by 9
“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
Nijhawan, A. K., Chan, A. M., Hsu, D. J., Chen, L. X., & Kohlstedt, K. L. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c05820 · Cited by 7
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
“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
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 6
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
The curse of dimensionality loses its power
Tuckerman, M. E. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00182-0 · Cited by 5
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
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
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
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
“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
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
“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 5
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
Cotter, S. L., Kevrekidis, I. G., & Russell, P. T. (2020) · SIAM/ASA Journal on Uncertainty Quantification · doi:10.1137/19m1239416 · Cited by 4
“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
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 5
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
“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
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
Self-citation
Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- enhanced sampling, free energy landscape, machine learning, molecular dynamics, adaptive biasing, collective variables, lipid saturation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
