The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
CHARMM: A program for macromolecular energy, minimization, and dynamics calculations
Abstract
CHARMM (C hemistry at HAR vard M acromolecular M echanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first‐ or second‐derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.
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
An algorithm for the machine calculation of complex Fourier series
Cooley, J. W., & Tukey, J. W. (1965) · Mathematics of Computation · doi:10.1090/s0025-5718-1965-0178586-1 · Cited by 13,154
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
A relational model of data for large shared data banks
Codd, E. F. (1970) · Communications of the ACM · doi:10.1145/362384.362685 · Cited by 5,547
Cited by
Items 101–119 of 119
Xie, C., Soeda, Y., Shinzaki, Y., In, Y., Tomoo, K., Ihara, Y., & Miyasaka, T. (2015) · Journal of Neurochemistry · doi:10.1111/jnc.13228 · Cited by 27
Antibody affinity maturation by computational design
Kuroda, D., & Tsumoto, K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8648-4_2 · Cited by 26
Samarut, E., Amal, I., Markov, G. V., Stote, R., Dejaegere, A., Laudet, V., & Rochette-Egly, C. (2011) · Molecular Biology and Evolution · doi:10.1093/molbev/msr035 · Cited by 26
Chebaro, Y., Amal, I., Rochel, N., Rochette-Egly, C., Stote, R. H., & Dejaegere, A. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003012 · Cited by 25
Nadh, A. G., Revikumar, A., Sudhakaran, P. R., & Nair, A. S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105422 · Cited by 22
Dynamics of chromosome organization in a minimal bacterial cell
Gilbert, B. R., Thornburg, Z. R., Brier, T. A., Stevens, J. A., Grünewald, F., Stone, J. E., Marrink, S. J., & Luthey-Schulten, Z. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1214962 · Cited by 19
Wang, Y., Qin, D., Jin, L., & Liang, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105410 · Cited by 19
Scoring Functions for Protein–Ligand Interactions
Sotriffer, C. (2012) · Protein‐Ligand Interactions · doi:10.1002/9783527645947.ch12 · Cited by 16
Craven, S. J., Condon, S. G. F., Díaz Vázquez, G., Cui, Q., & Senes, A. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.101460 · Cited by 15
Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.018 · Cited by 14
Investigation of the interactions between the EphB2 receptor and SNEW peptide variants
Ma, B., Kolb, S., Diprima, M., Karna, M., Tosato, G., Yang, Q., Huang, Q., & Nussinov, R. (2014) · Growth Factors · doi:10.3109/08977194.2014.985786 · Cited by 10
Abdollahi, H., Prestegard, J. H., & Valafar, H. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102655 · Cited by 8
Performance-Based Screening of Porous Materials for Carbon Capture
Farmahini, A. H., Krishnamurthy, S., Friedrich, D., Brandani, S., & Sarkisov, L. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01266 · Cited by 7
Molecular insights into human phosphatidylserine synthase 2 and its regulation of SREBP pathways
Li, D., Chen, H., Vale, G., Elghobashi-Meinhardt, N., Hatton, A., Rong, S., McDonald, J. G., & Li, X. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2501177122 · Cited by 6
Computational Approaches to Zeolite-Based Adsorption Processes
Gutiérrez-Sevillano, J. J., & Calero, S. (2020) · Structure and Bonding · doi:10.1007/430_2020_66 · Cited by 4
EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery
Wijeratne, H. R. S., Runnebohm, A. M., Barron, M. P., Hampton, A. J., Li, R., Peck Justice, S. A., Lenceski, S. H., Rooney, J. D., Smith-Kinnaman, W. R., Chang, G. S., et al. (2026) · doi:10.64898/2026.01.26.701775 · Cited by 1
Barron, M. P., Wijeratne, H. R. S., Runnebohm, A. M., Caric, K. M., Mosley, A. L., & Vilseck, J. Z. (2025) · doi:10.1101/2025.06.30.662390 · Cited by 1
Structural dynamics of IRE1 and its interaction with unfolded peptides
Spinetti, E., Ścibisz, G., Karagöz, G. E., & Covino, R. (2026) · eLife · doi:10.7554/elife.106716
Free energies and equilibria of peptide bond hydrolysis and formation.
Martin, R. B. (1998) · Biopolymers · doi:10.1002/(sici)1097-0282(19980415)45:5<351::aid-bip3>3.0.co;2-k
Metadata
- Authors
- Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S., & Karplus, M.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 1983-06
- Volume
- 4
- Issue
- 2
- Pages
- 187-217
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2162166182
- Cited by
- 14,944
- References
- 3
- FWCI
- 11.50
- 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.