scholarly journals Hugoniot of shocked liquid deuterium up to 300 GPa: Quantum molecular dynamic simulations

2010 ◽  
Vol 108 (4) ◽  
pp. 044909 ◽  
Author(s):  
Cong Wang ◽  
Xian-Tu He ◽  
Ping Zhang
Keyword(s):  
Molecular Dynamic ◽  
Molecular Dynamic Simulations ◽  
Dynamic Simulations ◽  
Quantum Molecular Dynamic

scholarly journals Quantum molecular dynamic simulations of warm dense carbon monoxide

2011 ◽  
Vol 135 (6) ◽  
pp. 064501 ◽  
Author(s):  
Yujuan Zhang ◽  
Cong Wang ◽  
Dafang Li ◽  
Ping Zhang
Keyword(s):  
Carbon Monoxide ◽  
Molecular Dynamic ◽  
Molecular Dynamic Simulations ◽  
Dynamic Simulations ◽  
Quantum Molecular Dynamic

scholarly journals Ranking the Efficiency of Gas Hydrate Anti-agglomerants through Molecular Dynamic Simulations

2021 ◽  
Vol 125 (5) ◽  
pp. 1487-1502
Author(s):  
Stephan Mohr ◽  
Felix Hoevelmann ◽  
Jonathan Wylde ◽  
Natascha Schelero ◽  
Juan Sarria ◽  
...  
Keyword(s):  
Molecular Dynamic ◽  
Gas Hydrate ◽  
Molecular Dynamic Simulations ◽  
Dynamic Simulations

scholarly journals Flow of long chain hydrocarbons through carbon nanotubes (CNTs)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pranay Asai ◽  
Palash Panja ◽  
Raul Velasco ◽  
Milind Deo
Keyword(s):  
Carbon Nanotubes ◽  
Molecular Dynamic ◽  
Continuum Models ◽  
Molecular Dynamic Simulations ◽  
Dynamic Simulations ◽  
Pharmaceutical Applications ◽  
Molecular Sizes ◽  
Hydrocarbon Molecules ◽  
Pressure Driven Flow ◽  
Long Chain

AbstractThe pressure-driven flow of long-chain hydrocarbons in nanosized pores is important in energy, environmental, biological, and pharmaceutical applications. This paper examines the flow of hexane, heptane, and decane in carbon nanotubes (CNTs) of pore diameters 1–8 nm using molecular dynamic simulations. Enhancement of water flow in CNTs in comparison to rates predicted by continuum models has been well established in the literature. Our work was intended to observe if molecular dynamic simulations of hydrocarbon flow in CNTs produced similar enhancements. We used the OPLS-AA force field to simulate the hydrocarbons and the CNTs. Our simulations predicted the bulk densities of the hydrocarbons to be within 3% of the literature values. Molecular sizes and shapes of the hydrocarbon molecules compared to the pore size create interesting density patterns for smaller sized CNTs. We observed moderate flow enhancements for all the hydrocarbons (1–100) flowing through small-sized CNTs. For very small CNTs the larger hydrocarbons were forced to flow in a cork-screw fashion. As a result of this flow orientation, the larger molecules flowed as effectively (similar enhancements) as the smaller hydrocarbons.

scholarly journals Exploring aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction: an in silico study

2021 ◽  
Author(s):  
Chiara Luise ◽  
Dina Robaa ◽  
Wolfgang Sippl
Keyword(s):  
Molecular Dynamic ◽  
In Silico ◽  
Binding Pocket ◽  
Binding Mode ◽  
Molecular Dynamic Simulations ◽  
Flexible Docking ◽  
Dynamic Simulations ◽  
Binding Mode Prediction ◽  
Aromatic Cage ◽  
The Right

AbstractSome of the main challenges faced in drug discovery are pocket flexibility and binding mode prediction. In this work, we explored the aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction by means of in silico approaches. We first investigated the Spindlin1 aromatic cage plasticity by analyzing the available crystal structures and through molecular dynamic simulations. Then we assessed the ability of rigid docking and flexible docking to rightly reproduce the binding mode of a known ligand into Spindlin1, as an example of a reader protein displaying flexibility in the binding pocket. The ability of induced fit docking was further probed to test if the right ligand binding mode could be obtained through flexible docking regardless of the initial protein conformation. Finally, the stability of generated docking poses was verified by molecular dynamic simulations. Accurate binding mode prediction was obtained showing that the herein reported approach is a highly promising combination of in silico methods able to rightly predict the binding mode of small molecule ligands in flexible binding pockets, such as those observed in some reader proteins.

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