The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automation of the CHARMM General Force Field (CGenFF) II: Assignment of Bonded Parameters and Partial Atomic Charges
Open accessAbstract
Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug candidates interacting with biological systems. In these simulations, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters, and partial atomic charges is required. In the present article, algorithms for the assignment of parameters and charges for the CHARMM General Force Field (CGenFF) are presented. These algorithms rely on the existing parameters and charges that were determined as part of the parametrization of the force field. Bonded parameters are assigned based on the similarity between the atom types that define said parameters, while charges are determined using an extended bond-charge increment scheme. Charge increments were optimized to reproduce the charges on model compounds that were part of the parametrization of the force field. A "penalty score" is returned for every bonded parameter and charge, allowing the user to quickly and conveniently assess the quality of the force field representation of different parts of the compound of interest. Case studies are presented to clarify the functioning of the algorithms and the significance of their output data.
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
Cited by
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
Antidepressant drugs act by directly binding to TRKB neurotrophin receptors
Casarotto, P. C., Girych, M., Fred, S. M., Kovaleva, V., Moliner, R., Enkavi, G., Biojone, C., Cannarozzo, C., Sahu, M. P., Kaurinkoski, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.034 · Cited by 663
Psychedelics promote plasticity by directly binding to BDNF receptor TrkB
Moliner, R., Girych, M., Brunello, C. A., Kovaleva, V., Biojone, C., Enkavi, G., Antenucci, L., Kot, E. F., Goncharuk, S. A., Kaurinkoski, K., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01316-5 · Cited by 478
Fatigue-resistant adhesion of hydrogels
Liu, J., Lin, S., Liu, X., Qin, Z., Yang, Y., Zang, J., & Zhao, X. (2020) · Nature Communications · doi:10.1038/s41467-020-14871-3 · Cited by 420
Molecular Determinants of BK Channel Functional Diversity and Functioning
Latorre, R., Castillo, K., Carrasquel-Ursulaez, W., Sepulveda, R. V., Gonzalez-Nilo, F., Gonzalez, C., & Alvarez, O. (2017) · Physiological Reviews · doi:10.1152/physrev.00001.2016 · Cited by 271
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-citationAn 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
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
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
Martin, N. E., Malik, S., Calimet, N., Changeux, J.-P., & Cecchini, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005784 · Cited by 38
Vuckovic, Z., Wang, J., Pham, V., Mobbs, J. I., Belousoff, M. J., Bhattarai, A., Burger, W. A., Thompson, G., Yeasmin, M., Nawaratne, V., et al. (2023) · eLife · doi:10.7554/elife.83477 · Cited by 34
Ketamine Metabolite (2 R,6 R)-Hydroxynorketamine Interacts with μ and κ Opioid Receptors
Joseph, T. T., Bu, W., Lin, W., Zoubak, L., Yeliseev, A., Liu, R., Eckenhoff, R. G., & Brannigan, G. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.0c00741 · Cited by 28
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-citationStructural insights into ligand recognition and activation of the succinate receptor SUCNR1
Liu, A., Liu, Y., Zhang, W., & Ye, R. D. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114381 · Cited by 18
How Robust Is the Ligand Binding Transition State?
Bose, S., Lotz, S. D., Deb, I., Shuck, M., Lee, K. S. S., & Dickson, A. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c08940 · Cited by 16
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2024) · eLife · doi:10.7554/elife.91973.4 · Cited by 14
Molecular mechanisms of SLC30A10-mediated manganese transport
Shen, X., Zhang, J. K., Sun, P., Zhong, H., He, R., Wang, S., Guo, X., & Yang, H. (2025) · Nature Communications · doi:10.1038/s41467-025-63616-7 · Cited by 14
A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor
Clayton, J., Shi, L., Robertson, M. J., Skiniotis, G., Michaelides, M., Stavitskaya, L., & Shen, J. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110437 · Cited by 12
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
MSBack: Multiscale Backmapping of Highly Coarse-Grained Proteins Using Constrained Diffusion
Waltmann, C., Wang, Y., Yang, C., Kim, S., & Voth, G. A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00459 · Cited by 11
Gao, Y.-D., Hu, Y., Crespo, A., Wang, D., Armacost, K. A., Fells, J. I., Fradera, X., Wang, H., Wang, H., Sherborne, B., et al. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0072-z · Cited by 9
Deganutti, G., Pipito, L., Rujan, R. M., Weizmann, T., Griffin, P., Ciancetta, A., Moro, S., & Reynolds, C. A. (2025) · eLife · doi:10.7554/elife.96513 · Cited by 7
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2023) · doi:10.1101/2023.09.05.556211 · Cited by 6
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2024) · doi:10.7554/elife.91973.3
Metadata
- Authors
- Vanommeslaeghe, K., Raman, E. P., & MacKerell, A. D.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-11-28
- Volume
- 52
- Issue
- 12
- Pages
- 3155-3168
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W1997772366
- Cited by
- 1,750
- References
- 1
- FWCI
- 47.51
- 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.