CHARMM

CHARMM (Chemistry at HARvard Macromolecular Mechanics). CHARMM models the dynamics and mechanics of macromolecular systems using empirical and mixed empirical/quantum mechanical force fields. CHARMM is designed to investigate the structure and dynamics of large molecules. It performs free energy calculations of mutations and drug binding as well as conformational folding of peptides. It uses classical mechanical methods to investigate potential energy surfaces derived from experimental and ”ab initio” quantum chemical calculations. In addition, mixed quantum mechanical/classical systems can be defined to investigate chemical processes such as enzyme catalysis.


References in zbMATH (referenced in 121 articles )

Showing results 1 to 20 of 121.
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  1. Hassan, Muhammad; Stamm, Benjamin: A linear scaling in accuracy numerical method for computing the electrostatic forces in the (N)-body dielectric spheres problem (2021)
  2. Jin, Shi; Li, Lei; Xu, Zhenli; Zhao, Yue: A random batch Ewald method for particle systems with Coulomb interactions (2021)
  3. Paul J. Atzberger: MLMOD Package: Machine Learning Methods for Data-Driven Modeling in LAMMPS (2021) arXiv
  4. Schiebl, Mark; Romero, Ignacio: Energy-momentum conserving integration schemes for molecular dynamics (2021)
  5. Song, Linlu; Ning, Shangbo; Hou, Jinxuan; Zhao, Yunjie: Performance of protein-ligand docking with CDK4/6 inhibitors: a case study (2021)
  6. T. L. Underwood, J. A. Purton, J. R. H. Manning, A. V. Brukhno, K. Stratford, T. Düren, N. B. Wilding, S. C. Parker: dlmontepython: A Python library for automation and analysis of Monte Carlo molecular simulations (2021) arXiv
  7. Bramer, David; Wei, Guo-Wei: Atom-specific persistent homology and its application to protein flexibility analysis (2020)
  8. Kraus, Johannes; Nakov, Svetoslav; Repin, Sergey: Reliable computer simulation methods for electrostatic biomolecular models based on the Poisson-Boltzmann equation (2020)
  9. Zhao, Rundong; Wang, Menglun; Chen, Jiahui; Tong, Yiying; Wei, Guo-Wei: The de Rham-Hodge analysis and modeling of biomolecules (2020)
  10. Andrew Abi-Mansour: PyGran: An object-oriented library for DEM simulation and analysis (2019) not zbMATH
  11. Friedrich, Manuel; Mainini, Edoardo; Piovano, Paolo; Stefanelli, Ulisse: Characterization of optimal carbon nanotubes under stretching and validation of the Cauchy-Born rule (2019)
  12. Younes Nejahi; Mohammad Soroush Barhaghi; Jason Mick; Brock Jackman; Kamel Rushaidat; Yuanzhe Li; Loren Schwiebert; Jeffrey Potoff: GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids (2019) not zbMATH
  13. Chen, Jiahui; Geng, Weihua: On preconditioning the treecode-accelerated boundary integral (TABI) Poisson-Boltzmann solver (2018)
  14. Worley, Bradley; Delhommel, Florent; Cordier, Florence; Malliavin, Thérèse E.; Bardiaux, Benjamin; Wolff, Nicolas; Nilges, Michael; Lavor, Carlile; Liberti, Leo: Tuning interval branch-and-prune for protein structure determination (2018)
  15. Zhong, Yimin; Ren, Kui; Tsai, Richard: An implicit boundary integral method for computing electric potential of macromolecules in solvent (2018)
  16. Fath, L.; Hochbruck, M.; Singh, C. V.: A fast mollified impulse method for biomolecular atomistic simulations (2017)
  17. Mainini, Edoardo; Murakawa, H.; Piovano, Paolo; Stefanelli, Ulisse: Carbon-nanotube geometries as optimal configurations (2017)
  18. Michael E. Fortunato, Coray M. Colina: pysimm: A python package for simulation of molecular systems (2017) not zbMATH
  19. Stefanelli, Ulisse: Stable carbon configurations (2017)
  20. Chen, Duan: A new Poisson-Nernst-Planck model with ion-water interactions for charge transport in ion channels (2016)

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Further publications can be found at: http://www.charmm.org/info/literature.html