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Molecular Mechanics (MM) is the method of choice for computational studies of biomolecular systems owing to its modest computational cost, which makes it possible to routinely perform molecular dynamics (MD) simulations on chemical systems of biophysical and biomedical relevance.

Scope of Review

As one of the main factors limiting the accuracy of MD results is the empirical force field used, the present paper offers a review of recent developments in the CHARMM additive force field, one of the most popular bimolecular force fields. Additionally, we present a detailed discussion of the CHARMM Drude polarizable force field, anticipating a growth in the importance and utilization of polarizable force fields in the near future. Throughout the discussion emphasis is placed on the force fields’ parametrization philosophy and methodology.

Major Conclusions

Recent improvements in the CHARMM additive force field are mostly related to newly found weaknesses in the previous generation of additive force fields. Beyond the additive approximation is the newly available CHARMM Drude polarizable force field, which allows for MD simulations of up to 1 microsecond on proteins, DNA, lipids and carbohydrates.

General Significance

Addressing the limitations ensures the reliability of the new CHARMM36 additive force field for the types of calculations that are presently coming into routine computational reach while the availability of the Drude polarizable force fields offers a model that is an inherently more accurate model of the underlying physical forces driving macromolecular structures and dynamics.


molecular dynamics, empirical force field, potential energy function, molecular mechanics, computer-aided drug design, biophysics

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017


Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) CHARMM additive and polarizable force fields for biophysics and computer-aided drug design in Preceding Membrane Neutralization using a Quantum coupled mutation finder for predicting functionally or structurally important sites and quantum Jensen-Shannon divergence CUDA programming multi-mimotopic algorithmic approach for biclustering analysis of expression data.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;


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