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WIREs Comput Mol Sci
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Quantitative predictions from molecular simulations using explicit or implicit interactions

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Abstract Equilibrium simulations of molecular systems allow to extract many physicochemical properties. Given an “accurate enough” model, a “large enough” simulation system and “long enough” simulations, such calculations should yield accurate predictions of properties that can be tested by experimental measurements. Non‐equilibrium simulations can be used as a tool to obtain specific properties like viscosity or conductivity, but they have the drawback that in general only one property per simulation is produced. In addition, a range of methods is available for computing free energy differences. We here review the state of the art of using classical simulation models for generating quantitative predictions. Popular force fields have significant predictive power already but there is room for improvement. Bonded force potentials may need to be replaced by more accurate ones to better reproduce vibrational frequencies. Simplification of non‐bonded force terms, such as cut‐offs for electrostatic or dispersion interactions, should be avoided. Routine usage of force field methods will therefore require some tuning of parameters. Despite the extensive toolbox that is available for producing quantitative results, the computational cost of explicit solvent simulation is significant and therefore, approximate methods like implicit solvent models remain popular and are still being developed. Based on fundamental arguments as well as on examples of solvation free energies, host–guest complexation and non‐covalent association of molecules in solution, we conclude that implicit solvents as well as algorithmic simplifications are most useful when validation using experimental data or rigorous theoretical treatments is possible. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Theoretical and Physical Chemistry > Statistical Mechanics Software > Simulation Methods
Section through the density of chloride ions around half a capsid of satellite tobacco mosaic virus, determined using (a) explicit solvent molecular dynamics simulations and (b) by solving the non‐linear Poisson Boltzmann equation. Reprinted with permission from Larsson and van der Spoel (2012). Copyright 2012. American Chemical Society41
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Surface tension for ≈ 140 liquids simulated using the CGenFF66 with and without the Lennard‐Jones PME method. Reprinted with permission from Fischer et al34
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Folded mini‐proteins from implicit solvent simulations. Reprinted with permission from Nguyen et al160
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Energetic decomposition along host‐guest distance (ξ) for [β‐CD:nabumetone] complex formation in explicit TIP3P water (left) and GB (right) implicit solvent models. (a) and (b) represent intra‐molecular interactions, (c) and (d) for intermolecular interactions, and (e) and (f) for entropy contributions. Reprinted with permission from Zhang et al154
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Drug‐like guest molecules of nabumetone inside β‐cyclodextrin monomer (left) and daidzin inside β‐cyclodextrin dimer (right)
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Correlation between polar contribution to solvation free energy calculated with explicit solvent (ΔGpolar‐Expl.) and that calculated with implicit solvent (ΔGpolar‐Impl.): PB (black), (a) Still (red), (b) HCT (green), (c) OBC‐I (blue), and (d) OBC‐II (orange)
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The 25 × 25 cross‐solvation matrices for four different force fields yield insight into systematic differences in prediction accuracy. Reprinted with permission from Kashefolgheta et al115
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Experimental and calculated infrared spectrum for 1,3‐oxazole. Reprinted with permission from Henschel et al65
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Software > Simulation Methods
Theoretical and Physical Chemistry > Statistical Mechanics
Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

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