The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling
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
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535
How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980
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-citationSmall molecules, big targets: drug discovery faces the protein–protein interaction challenge
Scott, D. E., Bayly, A. R., Abell, C., & Skidmore, J. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.29 · Cited by 1,137
Identification 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-citationSeparation of a mixture of independent signals using time delayed correlations
Molgedey, L., & Schuster, H. G. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3634 · Cited by 882
Tiwary, P., & Parrinello, M. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.230602 · Cited by 494
Visualization of transient encounter complexes in protein–protein association
Tang, C., Iwahara, J., & Clore, G. M. (2006) · Nature · doi:10.1038/nature05201 · Cited by 406
A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems
Noé, F., & Nüske, F. (2013) · Multiscale Modeling & Simulation · doi:10.1137/110858616 · Cited by 327
Self-citationEfficient Determination of Protein–Protein Standard Binding Free Energies from First Principles
Gumbart, J. C., Roux, B., & Chipot, C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400273t · Cited by 211
Cited by
Machine Learning for Molecular Simulation
Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956
Self-citationMarkov State Models: From an Art to a Science
Husic, B. E., & Pande, V. S. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.7b12191 · Cited by 886
VAMPnets for deep learning of molecular kinetics
Mardt, A., Pasquali, L., Wu, H., & Noé, F. (2018) · RePEc: Research Papers in Economics · doi:10.17169/refubium-964 · Cited by 465
Self-citationJankauskaitė, J., Jiménez-García, B., Dapkūnas, J., Fernández-Recio, J., & Moal, I. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty635 · Cited by 389
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Wehmeyer, C., & Noé, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5011399 · Cited by 401
Self-citation
Metadata
- Authors
- Plattner, N., Doerr, S., De Fabritiis, G., & Noé, F.
- Venue
- Nature Chemistry
- Published
- 2017-06-05
- Volume
- 9
- Issue
- 10
- Pages
- 1005-1011
- ISSN
- 1755-4330 (ISSN-L)
- 1755-4349
- OpenAlex
- W2623055862
- Cited by
- 345
- References
- 11
- FWCI
- 14.27
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. https://app.cochat.ai/papers/10.1038/nchem.2785
- The paper
- Plattner, N., Doerr, S., De Fabritiis, G., & Noé, F. (2017). Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nature Chemistry. https://doi.org/10.1038/nchem.2785
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.
