The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Transition networks for modeling the kinetics of conformational change in macromolecules
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.
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
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Robust Perron cluster analysis in conformation dynamics
Deuflhard, P., & Weber, M. (2005) · Linear Algebra and its Applications · doi:10.1016/j.laa.2004.10.026 · Cited by 506
Cited by
Markov models of molecular kinetics: Generation and validation
Prinz, J.-H., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J. D., Schütte, C., & Noé, F. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3565032 · Cited by 1,275
Self-citationIdentification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990
Self-citationEverything you wanted to know about Markov State Models but were afraid to ask
Pande, V. S., Beauchamp, K., & Bowman, G. R. (2010) · Methods · doi:10.1016/j.ymeth.2010.06.002 · Cited by 698
Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
Buch, I., Giorgino, T., & De Fabritiis, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1103547108 · Cited by 655
Schwantes, C. R., & Pande, V. S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300878a · Cited by 597
Wei, G., Xi, W., Nussinov, R., & Ma, B. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00562 · Cited by 436
To milliseconds and beyond: challenges in the simulation of protein folding
Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.11.002 · Cited by 409
Bowman, G. R., Beauchamp, K. A., Boxer, G., & Pande, V. S. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3216567 · Cited by 396
MSMBuilder2: Modeling Conformational Dynamics on the Picosecond to Millisecond Scale
Beauchamp, K. A., Bowman, G. R., Lane, T. J., Maibaum, L., Haque, I. S., & Pande, V. S. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct200463m · Cited by 372
Variational approach to molecular kinetics
Nüske, F., Keller, B. G., Pérez-Hernández, G., Mey, A. S. J. S., & Noé, F. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct4009156 · Cited by 308
Self-citationBowman, G. R., & Geissler, P. L. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209309109 · Cited by 279
Markov state models based on milestoning
Schütte, C., Noé, F., Lu, J., Sarich, M., & Vanden-Eijnden, E. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3590108 · Cited by 228
Self-citationOn the Approximation Quality of Markov State Models
Sarich, M., Noé, F., & Schütte, C. (2010) · Multiscale Modeling & Simulation · doi:10.1137/090764049 · Cited by 182
Self-citationEMMA: A Software Package for Markov Model Building and Analysis
Senne, M., Trendelkamp-Schroer, B., Mey, A. S. J. S., Schütte, C., & Noé, F. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300274u · Cited by 148
Self-citationVoelz, V. A., Jäger, M., Yao, S., Chen, Y., Zhu, L., Waldauer, S. A., Bowman, G. R., Friedrichs, M., Bakajin, O., Lapidus, L. J., et al. (2012) · Journal of the American Chemical Society · doi:10.1021/ja302528z · Cited by 140
The Free Energy Landscape of Small Molecule Unbinding
Huang, D., & Caflisch, A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002002 · Cited by 116
Noé, F., Doose, S., Daidone, I., Löllmann, M., Sauer, M., Chodera, J. D., & Smith, J. C. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004646108 · Cited by 116
Self-citationLin, Y.-S., Bowman, G. R., Beauchamp, K. A., & Pande, V. S. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2011.12.002 · Cited by 111
Zhuang, W., Cui, R. Z., Silva, D.-A., & Huang, X. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp109592b · Cited by 70
Hierarchical Nyström methods for constructing Markov state models for conformational dynamics
Yao, Y., Cui, R. Z., Bowman, G. R., Silva, D.-A., Sun, J., & Huang, X. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4802007 · Cited by 69
Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs
Nussinov, R., Zhang, M., Maloney, R., Liu, Y., Tsai, C.-J., & Jang, H. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167569 · Cited by 66
Markov models and dynamical fingerprints: Unraveling the complexity of molecular kinetics
Keller, B. G., Prinz, J.-H., & Noé, F. (2012) · Chemical Physics · doi:10.1016/j.chemphys.2011.08.021 · Cited by 59
Self-citationA Two-State Model for the Dynamics of the Pyrophosphate Ion Release in Bacterial RNA Polymerase
Da, L.-T., Pardo Avila, F., Wang, D., & Huang, X. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003020 · Cited by 54
Allosteric conformational ensembles have unlimited capacity for integrating information
Biddle, J. W., Martinez-Corral, R., Wong, F., & Gunawardena, J. (2021) · eLife · doi:10.7554/elife.65498 · Cited by 41
Xia, J., Deng, N.-J., & Levy, R. M. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp400797y · Cited by 29
Hazardous Shortcuts in Standard Binding Free Energy Calculations
Blazhynska, M., Goulard Coderc de Lacam, E., Chen, H., Roux, B., & Chipot, C. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01490 · Cited by 27
MSMExplorer: visualizing Markov state models for biomolecule folding simulations
Cronkite-Ratcliff, B., & Pande, V. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt051 · Cited by 25
Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models
Lindner, B., Yi, Z., Prinz, J.-H., Smith, J. C., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4824070 · Cited by 23
Self-citationBuilding Markov state models with solvent dynamics
Gu, C., Chang, H.-W., Maibaum, L., Pande, V. S., Carlsson, G. E., & Guibas, L. J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s2-s8 · Cited by 21
Yi, Z., Lindner, B., Prinz, J.-H., Noé, F., & Smith, J. C. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4824071 · Cited by 14
Self-citationSong, J., Gao, F., Cui, R. Z., Shuang, F., Liang, W., Huang, X., & Zhuang, W. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp304714q · Cited by 10
Mapping Protein Conformational Landscapes from Crystallographic Drug Fragment Screens
Saeed, A. A., Klureza, M. A., & Hekstra, D. R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01380 · Cited by 9
MasterMSM: A Package for Constructing Master Equation Models of Molecular Dynamics
de Sancho, D., & Aguirre, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00468 · Cited by 4
Metadata
- Authors
- Noé, F., & Fischer, S.
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2008-04
- Volume
- 18
- Issue
- 2
- Pages
- 154-162
- ISSN
- 0959-440X (ISSN-L)
- OpenAlex
- W1969903391
- Cited by
- 481
- References
- 2
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
