The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields
Open accessAbstract
The widely used CHARMM additive all‐atom force field includes parameters for proteins, nucleic acids, lipids, and carbohydrates. In the present article, an extension of the CHARMM force field to drug‐like molecules is presented. The resulting CHARMM General Force Field (CGenFF) covers a wide range of chemical groups present in biomolecules and drug‐like molecules, including a large number of heterocyclic scaffolds. The parametrization philosophy behind the force field focuses on quality at the expense of transferability, with the implementation concentrating on an extensible force field. Statistics related to the quality of the parametrization with a focus on experimental validation are presented. Additionally, the parametrization procedure, described fully in the present article in the context of the model systems, pyrrolidine, and 3‐phenoxymethylpyrrolidine will allow users to readily extend the force field to chemical groups that are not explicitly covered in the force field as well as add functional groups to and link together molecules already available in the force field. CGenFF thus makes it possible to perform “all‐CHARMM” simulations on drug‐target interactions thereby extending the utility of CHARMM force fields to medicinally relevant systems. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010
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
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
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
Cited by
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
Less is more: Sampling chemical space with active learning
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O., & Roitberg, A. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023802 · Cited by 811
Innovations and challenges in adsorption-based wastewater remediation: A comprehensive review
Satyam, S., & Patra, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29573 · Cited by 788
Han, L., Yan, L., Wang, M., Wang, K., Fang, L., Zhou, J., Fang, J., Ren, F., & Lu, X. (2018) · Chemistry of Materials · doi:10.1021/acs.chemmater.8b01446 · Cited by 438
Random heteropolymers preserve protein function in foreign environments
Panganiban, B., Qiao, B., Jiang, T., DelRe, C., Obadia, M. M., Nguyen, T. D., Smith, A. A. A., Hall, A., Sit, I., Crosby, M. G., et al. (2018) · Science · doi:10.1126/science.aao0335 · Cited by 294
Fribourg, M., Moreno, J. L., Holloway, T., Provasi, D., Baki, L., Mahajan, R., Park, G., Adney, S. K., Hatcher, C., Eltit, J. M., et al. (2011) · Cell · doi:10.1016/j.cell.2011.09.055 · Cited by 311
Self-citationMartini coarse-grained force field: Extension to RNA
Uusitalo, J. J., Ingólfsson, H. I., Marrink, S. J., & Faustino, I. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.05.043 · Cited by 259
Two 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
Using AMBER18 for Relative Free Energy Calculations
Song, L. F., Lee, T.-S., Zhu, C., York, D. M., & Merz, K. M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00105 · Cited by 234
Assessment of Binding Affinity via Alchemical Free-Energy Calculations
Kuhn, M., Firth-Clark, S., Tosco, P., Mey, A. S. J. S., Mackey, M., & Michel, J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00165 · Cited by 222
Unleashing the power of generative AI in drug discovery
Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195
Wang, H., Ma, Q., Qi, Y., Dong, J., Du, X., Rae, J., Wang, J., Wu, W.-F., Brown, A. J., Parton, R. G., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2018.11.014 · Cited by 175
Structural and Functional Analysis of a β2-Adrenergic Receptor Complex with GRK5
Komolov, K. E., Du, Y., Duc, N. M., Betz, R. M., Rodrigues, J. P. G. L. M., Leib, R. D., Patra, D., Skiniotis, G., Adams, C. M., Dror, R. O., et al. (2017) · Cell · doi:10.1016/j.cell.2017.03.047 · Cited by 152
A gating mechanism of pentameric ligand-gated ion channels
Calimet, N., Simoes, M., Changeux, J.-P., Karplus, M., Taly, A., & Cecchini, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1313785110 · Cited by 150
Mineral surface chemistry control for origin of prebiotic peptides
Erastova, V., Degiacomi, M. T., G. Fraser, D., & Greenwell, H. C. (2017) · Nature Communications · doi:10.1038/s41467-017-02248-y · Cited by 145
Vandermause, J., Xie, Y., Lim, J. S., Owen, C. J., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32294-0 · Cited by 147
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Self-citationMechanisms and inhibition of Porcupine-mediated Wnt acylation
Liu, Y., Qi, X., Donnelly, L., Elghobashi-Meinhardt, N., Long, T., Zhou, R. W., Sun, Y., Wang, B., & Li, X. (2022) · Nature · doi:10.1038/s41586-022-04952-2 · Cited by 128
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
“The parameters of the six ligands were determined according to the general CHARMM force field [34].”
MethodCSM-lig: a web server for assessing and comparing protein-small molecule affinities
Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw390 · Cited by 115
Ou, X., Xue, B., Lao, Y., Wutthinitikornkit, Y., Tian, R., Zou, A., Yang, L., Wang, W., Cao, Y., & Li, J. (2020) · Science Advances · doi:10.1126/sciadv.abb7620 · Cited by 109
Cao, X., Yap, J. L., Newell-Rogers, M. K., Peddaboina, C., Jiang, W., Papaconstantinou, H. T., Jupitor, D., Rai, A., Jung, K.-Y., Tubin, R. P., et al. (2013) · Molecular Cancer · doi:10.1186/1476-4598-12-42 · Cited by 87
Self-citationBFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations
Fu, H., Chen, H., Cai, W., Shao, X., & Chipot, C. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00269 · Cited by 83
An embedded lipid in the multidrug transporter LmrP suggests a mechanism for polyspecificity
Debruycker, V., Hutchin, A., Masureel, M., Ficici, E., Martens, C., Legrand, P., Stein, R. A., Mchaourab, H. S., Faraldo-Gómez, J. D., Remaut, H., et al. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-0464-y · Cited by 69
Li, T., Pantazes, R. J., & Maranas, C. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105954 · Cited by 79
Cryptic pocket formation underlies allosteric modulator selectivity at muscarinic GPCRs
Hollingsworth, S. A., Kelly, B., Valant, C., Michaelis, J. A., Mastromihalis, O., Thompson, G., Venkatakrishnan, A. J., Hertig, S., Scammells, P. J., Sexton, P. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11062-7 · Cited by 73
Jämbeck, J. P. M., & Lyubartsev, A. P. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp4111234 · Cited by 73
Hearon, S. E., Orr, A. A., Moyer, H., Wang, M., Tamamis, P., & Phillips, T. D. (2022) · Environmental Research · doi:10.1016/j.envres.2021.112433 · Cited by 65
Multiscale Modeling in the Clinic: Drug Design and Development
Clancy, C. E., An, G., Cannon, W. R., Liu, Y., May, E. E., Ortoleva, P., Popel, A. S., Sluka, J. P., Su, J., Vicini, P., et al. (2016) · Annals of Biomedical Engineering · doi:10.1007/s10439-016-1563-0 · Cited by 62
Tabatabaei, S. K., Pham, B., Pan, C., Liu, J., Chandak, S., Shorkey, S. A., Hernandez, A. G., Aksimentiev, A., Chen, M., Schroeder, C. M., et al. (2022) · Nano Letters · doi:10.1021/acs.nanolett.1c04203 · Cited by 63
A kinetic ensemble of the Alzheimer’s Aβ peptide
Löhr, T., Kohlhoff, K., Heller, G. T., Camilloni, C., & Vendruscolo, M. (2021) · Nature Computational Science · doi:10.1038/s43588-020-00003-w · Cited by 57
Pro-phagocytic function and structural basis of GPR84 signaling
Zhang, X., Wang, Y., Supekar, S., Cao, X., Zhou, J., Dang, J., Chen, S., Jenkins, L., Marsango, S., Li, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41201-0 · Cited by 46
Structural and thermodynamic framework for PIEZO1 modulation by small molecules
Jiang, W., Wijerathne, T. D., Zhang, H., Lin, Y.-C., Jo, S., Im, W., Lacroix, J. J., & Luo, Y. L. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310933120 · Cited by 44
Shifting redox reaction equilibria on demand using an orthogonal redox cofactor
Aspacio, D., Zhang, Y., Cui, Y., Luu, E., King, E., Black, W. B., Perea, S., Zhu, Q., Wu, Y., Luo, R., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01702-5 · Cited by 43
Kariuki, R., Penman, R., Bryant, S. J., Orrell-Trigg, R., Meftahi, N., Crawford, R. J., McConville, C. F., Bryant, G., Voïtchovsky, K., Conn, C. E., et al. (2022) · ACS Nano · doi:10.1021/acsnano.2c07751 · Cited by 41
Molecular basis of Wnt biogenesis, secretion, and Wnt7-specific signaling
Qi, X., Hu, Q., Elghobashi-Meinhardt, N., Long, T., Chen, H., & Li, X. (2023) · Cell · doi:10.1016/j.cell.2023.09.021 · Cited by 40
Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor
Batebi, H., Pérez-Hernández, G., Rahman, S. N., Lan, B., Kamprad, A., Shi, M., Speck, D., Tiemann, J. K. S., Guixà-González, R., Reinhardt, F., et al. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01334-2 · Cited by 38
A molecular propeller effect for chiral separation and analysis
Clemens, J. B., Kibar, O., & Chachisvilis, M. (2015) · Nature Communications · doi:10.1038/ncomms8868 · Cited by 31
Structural basis of HIV-1 maturation inhibitor binding and activity
Sarkar, S., Zadrozny, K. K., Zadorozhnyi, R., Russell, R. W., Quinn, C. M., Kleinpeter, A., Ablan, S., Meshkin, H., Perilla, J. R., Freed, E. O., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36569-y · Cited by 28
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
Structure and dynamics of differential ligand binding in the human ρ-type GABAA receptor
Cowgill, J., Fan, C., Haloi, N., Tobiasson, V., Zhuang, Y., Howard, R. J., & Lindahl, E. (2023) · Neuron · doi:10.1016/j.neuron.2023.08.006 · Cited by 25
Towards Development of Small Molecule Lipid II Inhibitors as Novel Antibiotics
Chauhan, J., Cardinale, S., Fang, L., Huang, J., Kwasny, S. M., Pennington, M. R., Basi, K., diTargiani, R., Capacio, B. R., MacKerell, A. D., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0164515 · Cited by 24
Self-citationA Rigorous Framework for Calculating Protein–Protein Binding Affinities in Membranes
Blazhynska, M., Gumbart, J. C., Chen, H., Tajkhorshid, E., Roux, B., & Chipot, C. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00941 · Cited by 22
Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation
Yu, W., Weber, D. J., & MacKerell, A. D. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00232 · Cited by 16
Self-citationQuantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy
Behera, S., Hahn, D. F., Wilson, C. J., Marsili, S., Tresadern, G., Gapsys, V., & de Groot, B. L. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00947 · Cited by 14
Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2
Zhu, C., Cao, S., Shang, T., Guo, J., Su, A., Li, C., & Duan, H. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf202 · Cited by 13
Gao, S., Wang, L., Bai, F., & Xu, S. (2023) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2023.108614 · Cited by 12
“Dividing and Conquering” and “Caching” in Molecular Modeling
Cao, X., & Tian, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095053 · Cited by 10
Computational studies on the functional and structural impact of pathogenic mutations in enzymes
Dissanayake, U. C., Roy, A., Maghsoud, Y., Polara, S., Debnath, T., & Cisneros, G. A. (2025) · Protein Science · doi:10.1002/pro.70081 · Cited by 8
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
Identification of the trail-following pheromone receptor in termites
Diallo, S., Kašparová, K., Šulc, J., Johny, J., Křivánek, J., Nebesářová, J., Sillam-Dussès, D., Kyjaková, P., Vondrášek, J., Machara, A., et al. (2025) · eLife · doi:10.7554/elife.101814 · Cited by 6
Bostancioglu, S. M., & Mutlu, O. (2025) · Parasitology Research · doi:10.1007/s00436-024-08433-5 · Cited by 4
Raza, S., Yadav, N. S., Jussupow, A., Kerfeld, C. A., Feig, M., & Vermaas, J. V. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c05178 · Cited by 4
Matsoukas, M.-T., Radomsky, T., Panagiotopoulos, V., Preez, R. D., Papadourakis, M., Tsianakas, K., Millar, R. P., Anderson, R. C., Spyroulias, G. A., & Newton, C. L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00577 · Cited by 4
Dai, J., Zhang, Y., Shi, C., Liu, Y., Xiu, P., & Wang, Y. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c06729 · Cited by 3
Quintana-Feliciano, R., Kottur, J., Ni, M., Ghosh, R., Salas-Estrada, L., Ahlsen, G., Rechkoblit, O., Shapiro, L., Filizola, M., Fang, G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52130-x · Cited by 3
Harnessing Photosynthetic ATP for Whole-Cell Biocatalysis in the Cyanobacterium Synechocystis
Loprete, G., Traverso, E., Vascon, F., Botteri, M., Robescu, M. S., Ubiali, D., Cendron, L., Morosinotto, T., & Bergantino, E. (2025) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c07236 · Cited by 2
Higinbotham, H. R., Arbour, C. A., & Imperiali, B. (2025) · Protein Science · doi:10.1002/pro.70352 · Cited by 2
Choi, J. (2023) · iScience · doi:10.1016/j.isci.2023.106911 · Cited by 1
Identification of the trail-following pheromone receptor in termites
Diallo, S., Kašparová, K., Šulc, J., Johny, J., Křivánek, J., Nebesářová, J., Sillam-Dussès, D., Kyjaková, P., Vondrášek, J., Machara, A., et al. (2025) · eLife · doi:10.7554/elife.101814.3
Pan, L., Zhang, Y., Wang, Z., Fan, C., You, Q., Wu, J., Feng, F., & Wang, X. (2026) · Archives of Microbiology · doi:10.1007/s00203-026-05034-x
Trichoderma swollenin activates AtABCB5-dependent auxin efflux to promote plant development
Lu, J., Liu, J., Jiang, S., Xu, Y., Yan, Z., Li, L., Zhang, F., Chen, W., Xu, D., Li, Y., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.001
Metadata
- Authors
- Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., & Mackerell, A. D.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2009-07-02
- Volume
- 31
- Issue
- 4
- Pages
- 671-690
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2070753604
- Cited by
- 7,509
- References
- 3
- FWCI
- 58.59
- Type
- journal article
- Language
- en
- Open access
- green
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.