CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Open accessAbstract
Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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One of the most powerful aspects of all-atom molecular dynamics is the transferability of the force fields to make predictions about interactions between molecules.
“One of the most powerful aspects of all-atom molecular dynamics is the transferability of the force fields to make predictions about interactions between molecules.54”
From Bringing the genetically minimal cell to life on a computer in 4D (2026) · cites this paper as [54]
The ‘Small Molecules’ section (page unavailable) describes assigning force-field parameters across compounds and reports successful validation on drug-like molecules, supporting the predictive use of force fields. But the paper’s explicit claim of high transferability concerns PRIMO, a coarse-grained model, rather than all-atom molecular dynamics; it does not establish transferability as one of all-atom MD’s most powerful aspects. (LLM, conf=0.78)
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CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field
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Self-citationSelf-guided Langevin dynamics simulation method
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Self-citationGeneralized correlation for biomolecular dynamics
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Self-citationThe GAMESS-UK electronic structure package: algorithms, developments and applications
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Self-citationDocking Covalent Inhibitors: A Parameter Free Approach To Pose Prediction and Scoring
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Self-citationNovel generalized Born methods
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Self-citationOvercoming free energy barriers using unconstrained molecular dynamics simulations.
Hénin, J., & Chipot, C. (2004) · Journal of Chemical Physics · doi:10.1063/1.1773132 · Cited by 419 · see citing works
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Self-citationMechanisms and Free Energies of Enzymatic Reactions
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Protein flexibility in ligand docking and virtual screening to protein kinases.
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Weak alignment offers new NMR opportunities to study protein structure and dynamics
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A method for determining reaction paths in large molecules: application to myoglobin
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Self-citationMolecular Dynamics Simulations of Membrane Permeability
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Self-citationThe implementation of a fast and accurate QM/MM potential method in Amber
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Self-citationConstant‐pH molecular dynamics using continuous titration coordinates
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Self-citationFinding transition pathways using the string method with swarms of trajectories.
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Self-citationA new approach to the rapid determination of protein side chain conformations.
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Force Field for Peptides and Proteins based on the Classical Drude Oscillator
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Self-citationCHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates
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Self-citationThe SCC-DFTB method and its application to biological systems
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Effect of Electrostatic Force Truncation on Interfacial and Transport Properties of Water
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Self-citationSimulation of Osmotic Pressure in Concentrated Aqueous Salt Solutions
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Self-citationDevelopment of a generalized Born model parameterization for proteins and nucleic acids
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Self-citationλ‐dynamics: A new approach to free energy calculations
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Self-citationActivation Energies from Bond Energies. I. Hydrogen Transfer Reactions
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Density functional tight binding
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CHARMM-GUI 10 Years for Biomolecular Modeling and Simulation
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Self-citationZeolite structure and reactivity by combined quantum-chemical- classical calculations
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A molecular dynamics study of polarizable water
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Self-citationNeural Network Potential Energy Surfaces for Small Molecules and Reactions
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Constant-pH molecular dynamics using stochastic titration
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Best Practices for Alchemical Free Energy Calculations [Article v1.0]
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Self-citationRandom walk in orthogonal space to achieve efficient free-energy simulation of complex systems
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Self-citationActive site dynamics in protein molecules: A stochastic boundary molecular‐dynamics approach
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Self-citationConstant pH molecular dynamics with proton tautomerism.
Khandogin, J., & Brooks, C. L. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.061341 · Cited by 286 · see citing works
Self-citationMechanical properties of lipid bilayers from molecular dynamics simulation
Venable, R., Brown, F. L. H., & Pastor, R. (2015) · Chemistry and Physics of Lipids · doi:10.1016/j.chemphyslip.2015.07.014 · Cited by 284
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Klauda, J. B., Brooks, B., MacKerell, A. D., Venable, R., & Pastor, R. (2005) · Journal of Physical Chemistry B · doi:10.1021/jp0468096 · Cited by 280 · see citing works
Self-citationStern, H. A., & Feller, S. E. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1537244 · Cited by 272 · see citing works
Gutiérrez, I. S., Lin, F.-Y., Vanommeslaeghe, K., Lemkul, J. A., Armacost, K., Brooks, C., & MacKerell, A. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.06.034 · Cited by 260 · see citing works
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Self-citationSwails, J. M., York, D., & Roitberg, A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct401042b · Cited by 258 · see citing works
FACTS: Fast analytical continuum treatment of solvation
Haberthür, U., & Caflisch, A. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20832 · Cited by 258 · see citing works
Self-citationJiang, W., Hardy, D., Phillips, J., MacKerell, A. D., Schulten, K., & Roux, B. (2011) · Journal of Physical Chemistry Letters · doi:10.1021/jz101461d · Cited by 256
Self-citationFree Energy Computations: A Mathematical Perspective
Lelièvre, T., Rousset, M., & Stoltz, G. (2010) · doi:10.1142/p579 · Cited by 253 · see citing works
PBEQ-Solver for online visualization of electrostatic potential of biomolecules
Jo, S., Vargyas, M., Vasko-Szedlar, J., Roux, B., & Im, W. (2008) · Nucleic Acids Res. · doi:10.1093/nar/gkn314 · Cited by 251 · see citing works
Self-citationEffective energy function for proteins in lipid membranes
Lazaridis, T. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10410 · Cited by 251 · see citing works
Self-citationProton binding to proteins: pK(a) calculations with explicit and implicit solvent models.
Simonson, T., Carlsson, J., & Case, D. (2004) · Journal of the American Chemical Society · doi:10.1021/ja039788m · Cited by 247 · see citing works
Structural Mechanism of Voltage-Dependent Gating in an Isolated Voltage-Sensing Domain
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Self-citationE, W., Ren, W.-Q., & Vanden-Eijnden, E. (2005) · Chemical Physics Letters · doi:10.1016/j.cplett.2005.07.084 · Cited by 236 · see citing works
Fast-growth thermodynamic integration: Error and efficiency analysis
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Lindahl, E., & Edholm, O. (2000) · doi:10.1063/1.1287423 · Cited by 229
Flagged first, then by citations
2 of the 734 references couldn’t be matched to a known paper.
Research works printed but not linked: 86
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“All simulations were run with the CHARMM program with provisions to use machine-learned energy functions [27 – 30].”
InfluentialSelf-citationDisrupting Amyloid Filaments of Tau by Means of Electric Fields
Vargas-Rosales, P. A., Giangreco, G., & Caflisch, A. (2025) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c04393 · Cited by 2 · see citing works
Self-citationHwang, W., Gonzales, J. E., & Brooks, B. R. (2025) · Journal of Chemical Physics · doi:10.1063/5.0264936 · Cited by 2 · see citing works
Self-citationPolyPal: A Python Package for Molecular Dynamics Simulation of Amorphous Polymers
Warndorf, M. C., Swager, T., & Alexander-Katz, A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00254 · Cited by 2
Xu, J., Wang, W., Lee, J., & Park, Y. (2025) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2025.2477143 · Cited by 2
“With the obtained 1,000 docking poses, clustering based on the center of mass (COM) was performed using CHARMM (Chemistry at HARvard Macromolecular Mechanics; http:// www.charmm.org) (Hwang et al., 2024) to group these conformations.”
Self-citationStructure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions
Töpfer, K., Wang, J.-C., Patel, S., & Meuwly, M. (2025) · doi:10.48550/arxiv.2502.21233 · Cited by 2
“51,52 The molar composition of the mixtures was changed by varying the number of water and acetamide molecules while keeping constant the total concentration of K + and SCN − , see Table S1.”
Self-citationLoad-based divergence in the dynamic allostery of two TCRs recognizing the same pMHC
Chang-Gonzalez, A., Aoi, A., Mallis, R., Lang, M. J., Reinherz, E., & Hwang, W. (2024) · bioRxiv · doi:10.1101/2024.10.16.618634 · Cited by 2
“V 𝛽 contacts in B7 0 ( Figure 3 A) is consistent with the increase in total intra-TCR contact occupancy (horizontal bar in Figure 1 D bottom panel).”
Self-citationMohammadi, E., & Lemkul, J. A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01733 · Cited by 1
Self-citationMichel, H., Brown, A. M., MacKerell, A. D., & Lemkul, J. A. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag871 · Cited by 1 · see citing works
“The two DNA structures (DDX and TGQ) were obtained from the RCSB PDB and prepared using the CHARMM program [ 30 ], following the methods of our previous work [ 31–33 ].”
Self-citationMicroscopic Stress in Biomembranes: A Perspective on Key Concepts, Methods, and Applications.
Pirhadi, E., Lewis, A. L., Vanegas, J. M., & Yong, X. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00510 · Cited by 1
Korn, V., Rindfleisch, T., Posch, S., Underhaug, J., Horner, A., Miettinen, M. S., & Pluhackova, K. (2026) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c00779 · Cited by 1
BilboMD: a web-accessible SAXS and AlphaFold-guided modeling pipeline
Classen, S., Mundo, J. T. D., Kulkarni, D. D., Prabhakar, S., Hicks, A., & Hammel, M. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag377 · Cited by 2 · see citing works
“Historically, we have depended on CHARMM [ 31 , 32 ] for all MD steps, including conformational sampling.”
Efficient and Equivariant Prediction of Distributed Charges for Accurate Molecular Electrostatics.
Boittier, E. D., & Meuwly, M. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00248 · Cited by 1
Self-citationKramer, L. J., Austin, S. L., Majumdar, A., Smith, N. D., Woodcock, H. L., & Meyers, C. F. F. (2026) · Biochemistry · doi:10.1021/acs.biochem.6c00205 · Cited by 1 · see citing works
Self-citationElbeltagi, S., Mansour, S., Alanazi, M. A., El-Tohamy, H., Ismail, A. M., & Al-Theyab, N. S. (2026) · Dyes and pigments · doi:10.1016/j.dyepig.2026.113816 · Cited by 1 · see citing works
Induced Dipole Calculation with E(3)-Equivariant Neural Networks and Multipole Field Perturbation.
Yang, S.-Y., & Huang, J. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c02167 · Cited by 1 · see citing works
Self-citationRingel, M., Helmer, C. P. O., Zev, S., Driller, R., Buhr, E., Reinbold, M., Schwartz, R., Foley, G., Bodén, M., Garbe, D., et al. (2026) · FEBS Letters · doi:10.1002/1873-3468.70325 · Cited by 1
“We performed mechanistic docking using EnzyDock for all intermediate states in routes to both IE and HP (Scheme S1) [38,39] The EnzyDock program is based on the CHARMM simulation platform [40 – 42] and uses simulated annealing molecular dynamics (MD) and Monte Carlo (MC) sampling.”
Self-citationKäser, S., Koner, D., & Meuwly, M. (2026) · Precision Chemistry · doi:10.1021/prechem.5c00074 · Cited by 1
Self-citationInvestigating the Electrostatics Underlying Activation of the β2 Adrenergic Receptor
Montgomery, J. M., & Lemkul, J. A. (2026) · Biochimica et Biophysica Acta - Biomembranes · doi:10.1016/j.bbamem.2026.184503 · Cited by 1 · see citing works
Self-citationHow medically important antimicrobials bind to the 30S ribosomal subunit in a bacterial pathogen
Manjari, S. R., Mallery, C., & Banavali, N. K. (2025) · doi:10.64898/2025.12.24.696192 · Cited by 1 · see citing works
“In the present study, a gentle minimization is carried out using the CHARMM energy function 35 while the binding free energy is estimated using the Autodock Vina energy function 39 .”
Weighted Ensemble Simulations With the Drude Polarizable Model
Polêto, M. D., da Silva, G. D., Rubenstein, B., & Lemkul, J. A. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70264 · Cited by 1
Self-citationGao, J.-L., Huang, G. S., Simon, A., Caballero, E., Chen, K., Fahrenbruch, M. Z., Fairbourn, D., Harreschou, I., Kauffman, S., Thoma, C., et al. (2025) · Wiley Interdisciplinary Reviews. Computational Molecular Science · doi:10.1002/wcms.70053 · Cited by 1
“All free energy and PI simulations were performed using CHARMM [27] and were duplicated using QBICS [17], both of which are freely available to academic users.”
Self-citationSimultaneous Construction of Free Energy Surfaces Via Multisite λ Dynamics and Umbrella Sampling
Lai, T. T., & Brooks, C. L. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00807 · Cited by 1
Self-citationFrom Brownian dynamics to Poisson-Nernst-Planck equations: multi-resolution simulations of ions
Zhang, J., & Erban, R. (2025) · doi:10.48550/arxiv.2506.19738 · Cited by 1
“The Lennard-Jones parameters A j and B j are calculated using the values in the CHARMM force field [27].”
Core Flipping in Lead Optimization: Rank Ordering Using λ‑Dynamics
Gartan, P., Brooks, C. L., & Reuter, N. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00320 · Cited by 1 · see citing works
“Efficient sampling is achieved with the use of the adaptive landscape flattening algorithm (ALF), 42,43 already available/ implemented as part of the MS λ D workflow in the CHARMM 44,45 package.”
Self-citationMass-Zero constrained molecular dynamics for electrostatic interactions
Troni, F., Grassano, D., Narayan, J., Roux, B. T., & Bonella, S. (2025) · Journal of Chemical Physics · doi:10.1063/5.0283356 · Cited by 1
Self-citationStructure-Based Optimization of TBK1 Inhibitors.
Sun, W., Xie, Y., Xia, Q., Wang, Y.-X., Qi, X.-B., & Huang, N. (2025) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.4c00636 · Cited by 1 · see citing works
MPID polarizable force field: GPU implementation and biomolecular simulations.
Tan, Q.-Z., Simmonett, A. C., Brooks, B. R., & Huang, J. (2026) · Journal of Chemical Physics · doi:10.1063/5.0345546
Self-citationResidue-Position-Dependent Modulation of Urethanase Activity for Polyurethane Depolymerization
Zheng, M.-N., Liu, J.-W., Zhu, X.-M., Liang, C.-H., Li, Z.-Q., Zhang, Q.-Z., Zhang, W.-X., Wang, W.-X., Wei, R., Dong, W.-L., et al. (2026) · ACS Catalysis · doi:10.1021/acscatal.6c04525
Liu, Z.-Y., Yuan, Y.-N., Zhang, R.-S., & Su, W. (2026) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c04920
Time‐Dependent Voronoi Analysis of Amino Acid Side‐Chain Packing in Simulations
Bettencourt, S. B., & Hwang, W. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70499
Self-citationJung, J., Torre, D. U. L., Kobayashi, C., Ozaki, K., & Sugita, Y. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70492
A Fast and Parallelized Procedure for Checking Molecular Dynamics 3D Structure
Roe, D. R., & Brooks, B. R. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70494
Self-citationWang, B.-C., Dong, N.-H., Xue, S.-Y., Zhao, Y., Ji, C., Zhu, B.-W., & Zhang, S.-F. (2026) · Foods · doi:10.3390/foods15173118
“This computational strategy provides valuable insights into the structural basis of enzyme function and facilitates the targeted engineering of enzyme specificity and stability [14].”
Atomistic TCR-pMHC interactions bias CD8 memory fate within a single antigen-specific repertoire
Akitsu, A., Tan, K., Mallis, R. J., Booker, M. A., Duke‐Cohan, J. S., Brazin, K. N., Kirkpatrick, E. H., Parkins, A., Seabury, A. G., Aryal, S., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117908
Self-citationSundaresan, S., Kumar, R. R., & Rathinavelan, T. (2026) · Carbohydrate Research · doi:10.1016/j.carres.2026.110106
Self-citationAbdelaziz, A. E., Shokeir, A., Awad, B. I., & El-khawaga, O. (2026) · Naunyn-Schmiedeberg's Archives of Pharmacology · doi:10.1007/s00210-026-05782-z
Sawdekar, H., Mignon, J., Gondelaud, F., Walimbe, A., Mukhopadhyay, S., Chamieh, J., & Longhi, S. (2026) · Protein Science · doi:10.1002/pro.70758
Thodika, A. R. A., Panda, S., Pan, X.-L., Shao, Y.-H., & Nam, K. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00798
Self-citationOvchinnikov, V., & Karplus, M. (2026) · Antibodies · doi:10.3390/antib15040064
“Coordinate manipulation and minimization was performed using the program CHARMM (v.47b1) [44].”
Self-citationBroel, N., Wengner, F. V., Stein, J., Popa, F., Janssen, S., Spröer, C., Bunk, B., Xin, F.-J., Li, B.-L., & Gand, M. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.6c08619
Molecular basis of UV lesion binding and repair inhibition by ETS-family transcription factors
Sivapragasam, S., Terrell, J. R., van der Vaart, A., Germann, M. W., Laughery, M. F., Everly, M. E., Adhikari, S. P., Hrdlicka, P. J., Wyrick, J. J., & Poon, G. M. K. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag711 · Cited by 1 · see citing works
“H-bond and contact analyses were carried out using CHARMM [ 56 ]; visual analyses with VMD [ 57 ].”
Comparing Enhanced Sampling Methods in Exploring the Conformational Space of β-Catenin 17–48
Pineda, L. I. G., Polêto, M. D., Michel, H. M., Goodberlet, A., & Lemkul, J. A. (2026) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c02031
Self-citationPredicting human mRNA isoform levels from site-specific splicing kinetics in silico
Thornburg, Z. R., Song, Y.-J., Yan, J., Prasanth, K., & Bhargava, R. (2026) · bioRxiv · doi:10.64898/2026.06.16.732643
Tan, X., Liu, H., Liu, Q., Hu, D., Cui, J., Bai, W., Gand, M., & Li, B.-L. (2026) · Food Chemistry · doi:10.1016/j.foodchem.2026.150122
CDK5-p25 Peptide Inhibitor Samples an Intrinsically Disordered Ensemble in Solution.
Ramamurthy, A., Mondal, K., & Klauda, J. B. (2026) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c01887
Self-citationSchulz, J. M., Reynolds, R. C., & Schürer, S. (2026) · European journal of medicinal chemistry · doi:10.1016/j.ejmech.2026.119038
Tabor, W., Szczepańska, A., Theodoropoulou, N., Andrianopoulou, E., Piasecki, T., Krzyżek, P., Sołtysiak, K., Berlicki, Ł., Vassiliou, S., & Grabowiecka, A. (2026) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2026.146713
Malewicz, K., Robinson, K. E., Brown, A. M., Jeffrey, C. S., Philbin, C. S., McGlothlin, J., Lemkul, J. A., & Feldman, C. (2026) · bioRxiv · doi:10.64898/2026.05.20.726542
“We then minimized the Na V 1.4-TTX system via 5000 steps of steepest descent via the CHARMM program (Hwang et al. 2024) and then equilibrated in OpenMM 7.5.1 (Eastman et al. 2017) for 50 ns following the CHARMM-GUI Membrane Builder protocol.”
InfluentialSelf-citationYin, C.-T., Willitsch, S., & Meuwly, M. (2026) · Journal of Physical Chemistry A · doi:10.1021/acs.jpca.6c03146
Self-citationHua, X., Hu, Y.-T., Hu, W., Guo, J., & Xu, Y. (2026) · Biotechnology and Bioengineering · doi:10.1002/bit.70236
Molecular structure and DNA binding mode of unsymmetric cyanine dyes RiboGreen and OliGreen
Blackford, N., Nepal, S., He, H., Zheng, L.-Q., Yang, W., & Silvers, R. (2026) · bioRxiv · doi:10.1093/nar/gkag923
“All the simulations were performed using a modified version of the CHARMM [ 56 ] program developed by the Yang group.”
Zero-Shot Generation of Protein Conformational Ensembles Through AlphaFold Latent FloodingPreprint
Qian, R., Zhan, R., Song, Z., & Huang, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.16.718914
Self-citationINFLUENCE OF WATER ENVIRONMENT ON PROTEIN FOLDING AND COMPLEXATION
Roterman, I., Stąpor, K., Dułak, D., & Konieczny, L. (2026) · F1000Research · doi:10.12688/f1000research.178873.1
Fidelity of Machine-Learned Potentials: Quantitative Assessment for Protonated Oxalate
Qu, C., Houston, P. L., Yu, Q., Nandi, A., Bowman, J., Andreichev, V., Kaser, S., & Meuwly, M. (2026) · PRX Intelligence · doi:10.1103/jfjx-djk2
Self-citationMaking Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS
Pennati, L., Hu, A., Peng, I. B., Müllender, L., & Markidis, S. (2026) · ISC High Performance (ISC) · doi:10.23919/isc.2026.11520498 · Cited by 2 · see citing works
Payal, Thirman, J., Edmonds, K., Mishra, S., Blackwell, N., Zubi, Y. S., Roux, B., & Lewis, J. C. (2026) · ChemBioChem · doi:10.1002/cbic.70349
Self-citationBerta, D., Csóka, J., Samu, G., & Nagy, P. R. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c02128
Sampling the Grand Canonical Ensemble with Multisite λ Dynamics
Lai, T. T., & Brooks, C. L. (2026) · Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6c00493
Self-citationArchana, Charde, V., Kumar, V., Ranade, A., Meena, A. K., Srikanth, N., Acharya, R., & Mallajosyula, S. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70351
Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations.
Chaton, K. L., Boittier, E. D., Devereux, M., & Meuwly, M. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00589
Self-citationLatent space design of interatomic potentials
Atlas, S. (2026) · doi:10.48550/arxiv.2603.05655
“Prominent examples include the CHARMM 150,151 and Amber 152,153 force fields.”
Efficient, Equivariant Predictions of Distributed Charge Models
Boittier, E. D., & Meuwly, M. (2026) · doi:10.48550/arxiv.2602.07234
“78 Finally, the capability for applying these models in a TL-setting is investigated.”
Self-citation
- Authors
- Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., Choi, Y. K., Chu, J.-W., Crowley, M. F., Cui, Q., Damjanovic, A., Deng, Y., Devereux, M., Ding, X., Feig, M. F., Gao, J., Glowacki, D. R., Gonzales, J. E., Hamaneh, M. B., Harder, E. D., Hayes, R. L., Huang, J., Huang, Y., Hudson, P. S., Im, W., Islam, S. M., Jiang, W., Jones, M. R., Käser, S., Kearns, F. L., Kern, N. R., Klauda, J. B., Lazaridis, T., Lee, J., Lemkul, J. A., Liu, X., Luo, Y., MacKerell, A. D., Major, D. T., Meuwly, M., Nam, K., Nilsson, L., Ovchinnikov, V., Paci, E., Park, S., Pastor, R. W., Pittman, A. R., Post, C. B., Prasad, S., Pu, J., Qi, Y., Rathinavelan, T., Roe, D. R., Roux, B., Rowley, C. N., Shen, J., Simmonett, A. C., Sodt, A. J., Töpfer, K., Upadhyay, M., van der Vaart, A., Vazquez-Salazar, L. I., Venable, R. M., Warrensford, L. C., Woodcock, H. L., Wu, Y., Brooks, C. L., Brooks, B. R., & Karplus, M.
- Affiliations
- Korea Institute for Advanced Study (KR)
- University of South Florida (US)
- Centre National de la Recherche Scientifique (FR)
- University of Basel (CH)
- University of Vienna (AT)
- Case Western Reserve University (US)
- University of Michigan (US)
- University of Zurich (CH)
- National Yang Ming Chiao Tung University (TW)
- Chinese Academy of Sciences (CN)
- Lehigh University (US)
- National Laboratory of the Rockies (US)
- Boston University (US)
- National Institutes of Health (US)
- Shanghai R&D Center, DP Technology, Ltd., Shanghai 201210, China
- Tufts University (US)
- Michigan State University (US)
- University of Minnesota (US)
- Universidade de Santiago de Compostela (ES)
- Department of Physiology and Biophysics, Case Western Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
- Schrodinger (United States) (US)
- University of California, Irvine (US)
- Westlake University (CN)
- Jimei University (CN)
- Pfizer (United States) (US)
- Delaware State University (US)
- Argonne National Laboratory (US)
- University of Maryland, College Park (US)
- City College of New York (US)
- Korea Research Institute of Bioscience and Biotechnology (KR)
- Virginia Tech (US)
- Western University of Health Sciences (US)
- University of Maryland, Baltimore (US)
- Bar-Ilan University (IL)
- The University of Texas at Arlington (US)
- Karolinska Institutet (SE)
- Harvard University (US)
- University of Bologna (IT)
- Purdue University West Lafayette (US)
- Indiana University Indianapolis (US)
- Fudan University (CN)
- Indian Institute of Technology Hyderabad (IN)
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
- Carleton University (CA)
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
- Corresponding author
- Hwang, Wonmuk
- Brooks, Charles L.
- Brooks, Bernard R.
- ORCID
- Hwang, Wonmuk
- Austin, Steven L.
- Blondel, Arnaud
- Boittier, Eric D.
- Boresch, Stefan
- Buck, Matthias
- Caflisch, Amedeo
- Cheng, Xi
- Choi, Yeol Kyo
- Chu, Jhih-Wei
- Crowley, Michael F.
- Cui, Qiang
- Damjanovic, Ana
- Deng, Yuqing
- Devereux, Mike
- Ding, Xinqiang
- Feig, Michael F.
- Gao, Jiali
- Glowacki, David R.
- Gonzales, James E.
- Hamaneh, Mehdi Bagerhi
- Harder, Edward D.
- Hayes, Ryan L.
- Huang, Jing
- Huang, Yandong
- Hudson, Phillip S.
- Im, Wonpil
- Islam, Shahidul M.
- Jiang, Wei
- Jones, Michael R.
- Käser, Silvan
- Kearns, Fiona L.
- Kern, Nathan R.
- Klauda, Jeffery B.
- Lazaridis, Themis
- Lee, Jinhyuk
- Lemkul, Justin A.
- Liu, Xiaorong
- Luo, Yun
- MacKerell, Alexander D.
- Major, Dan T.
- Meuwly, Markus
- Nam, Kwangho
- Nilsson, Lennart
- Ovchinnikov, Victor
- Paci, Emanuele
- Park, Soohyung
- Pastor, Richard W.
- Post, Carol Beth
- Prasad, Samarjeet
- Pu, Jingzhi
- Qi, Yifei
- Rathinavelan, Thenmalarchelvi
- Roe, Daniel R.
- Roux, Benoit
- Rowley, Christopher N.
- Shen, Jana
- Simmonett, Andrew C.
- Sodt, Alexander J.
- Töpfer, Kai
- Upadhyay, Meenu
- van der Vaart, Arjan
- Vazquez-Salazar, Luis Itza
- Warrensford, Luke C.
- Woodcock, H. Lee
- Brooks, Charles L.
- Brooks, Bernard R.
- Karplus, Martin
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-09-20
- Volume
- 128
- Issue
- 41
- Pages
- 9976-10042
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- PMC
- PMC11492285
- OpenAlex
- W4402681508
- Cited by
- 136
- References
- 734
- Citations by year
- 2026: 73 · 2025: 54 · 2024: 9
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Physical Chemistry B (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VTechWorks (Virginia Tech) (publishedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- Knowledge@UChicago (University of Chicago) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Funders
- NCCR Catalysis
- European Research Council (866559)
- Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (200020-188724, 310030-212195)
- U.S. National Library of Medicine
- Office of Science (DE-AC02-06CH11357)
- Universit?t Basel
- National Institutes of Health (R01GM039478, R01GM046736, R01GM129519, R01GM132481, R01GM135392, R01GM138472, R01GM140316, R01GM143810, R21GM148895, R35GM126948, R35GM131710, R35GM137058, R35GM141930, R35GM148261, ZIA HL000340, ZIA HL001051, ZIA HL001052)
- National Science Foundation (MCB-1855942, MCB-1919096, MCB-211172)
- National Science and Technology Council (112-2113-M-A49-018-MY3)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- CHARMM, molecular dynamics simulation, simulation engine, user interface, force fields, quantum mechanical methods, atomistic simulation, coarse-grained simulation, biomolecular systems, computational biophysics
- MeSH terms
- Quantum Theory, Software, Molecular Dynamics Simulation
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