Applications of Molecular Modeling in the Study of Small Molecules (N2, O2, Rare Gas) Interactions with Complex Molecular Systems Like Zeolites and Macrocycles

1996 ◽  
Vol 51 (1) ◽  
pp. 81-94 ◽  
Author(s):  
C. Mellot ◽  
J. Lignieres ◽  
P. Pullumbi ◽  
R. Guilard
Keyword(s):  
Molecular Modeling ◽  
Small Molecules ◽  
Rare Gas ◽  
Molecular Systems ◽  
Gas Interactions

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2021 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Force Fields ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
State Models ◽  
Molecular Science

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

scholarly journals Molecular Modeling to Estimate the Diffusion Coefficients of Drugs and Other Small Molecules

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5340
Author(s):  
Shuichi Miyamoto ◽  
Kazumi Shimono
Keyword(s):  
Diffusion Coefficient ◽  
Molecular Modeling ◽  
Small Molecules ◽  
Einstein Equation ◽  
Diffusion Coefficients ◽  
Small Deviation ◽  
Molecular Transport ◽  
Diffusion Rates ◽  
Van Der Waals Radii ◽  
Potential Use

Diffusion is a spontaneous process and one of the physicochemical phenomena responsible for molecular transport, the rate of which is governed mainly by the diffusion coefficient; however, few coefficients are available because the measurement of diffusion rates is not straightforward. The translational diffusion coefficient is related by the Stokes–Einstein equation to the approximate radius of the diffusing molecule. Therefore, the stable conformations of small molecules were first calculated by molecular modeling. A simple radius rs and an effective radius re were then proposed and estimated using the stable conformers with the van der Waals radii of atoms. The diffusion coefficients were finally calculated with the Stokes–Einstein equation. The results showed that, for the molecules with strong hydration ability, the diffusion coefficients are best given by re and for other compounds, rs provided the best coefficients, with a reasonably small deviation of ~0.3 × 10−6 cm2/s from the experimental data. This demonstrates the effectiveness of the theoretical estimation approach, suggesting that diffusion coefficients have potential use as an additional molecular property in drug screening.

scholarly journals Molecular Modeling Studies of Some Uracil and New Deoxyuridine Derivatives

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yousra Abdel-Mottaleb ◽  
M. S. A. Abdel-Mottaleb
Keyword(s):  
Molecular Modeling ◽  
Density Functional ◽  
Biological Activities ◽  
Quantitative Relationship ◽  
Molecular Properties ◽  
Molecular Systems ◽  
Biological Potential ◽  
Td Dft ◽  
Molecular Modeling Studies ◽  
Bioactive Agents

Molecular modeling results reported in this paper are crucial in highlighting the quantitative relationship between the optimized structure and computed molecular properties related to four newly synthesized uracil derivatives with promising biological potential as anticancer bioactive agents. Moreover, 5-fluorouracil (5-FU) and its tautomers and thiouracils molecular properties are studied and correlated with their biological activities. The great medical importance of these and similar molecular systems requires research on their quantitative structure-activity relationships (QSAR) in order to further improve our knowledge about how receptor binding, selectivity, and pharmacological effects are achieved. Modeling is performed in the ground and the first singlet excited states using density functional theory (DFT) and its time-dependent extension (TD-DFT), respectively.

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