Journal of Computational Chemistry

Cover image for Vol. 37 Issue 20

Edited By: Charles L. Brooks III, Masahiro Ehara, Gernot Frenking, and Peter R. Schreiner

Impact Factor: 3.648

ISI Journal Citation Reports © Ranking: 2015: 40/163 (Chemistry Multidisciplinary)

Online ISSN: 1096-987X

Associated Title(s): International Journal of Quantum Chemistry, Wiley Interdisciplinary Reviews: Computational Molecular Science

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Recently Published Articles

  1. Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility

    Kyrylo Klimenko, Victor Kuz'min, Liudmila Ognichenko, Leonid Gorb, Manoj Shukla, Natalia Vinas, Edward Perkins, Pavel Polishchuk, Anatoly Artemenko and Jerzy Leszczynski

    Version of Record online: 24 JUN 2016 | DOI: 10.1002/jcc.24424

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    Solubility in water () is one of the key physico-chemical properties which can vary due to temperature change. Since experimental determination of solubility can be difficult, expensive, and time-consuming, QSPR modeling was used for organic compounds aqueous solubility prediction in temperature range 4-97°C. Feature net technic helped implementing solubility parameter k from linear regression equation for better model performance. Models have acceptable predictive capability comparing to COSMO-RS quantum chemical calculations.

  2. On the use of mass scaling for stable and efficient simulated tempering with molecular dynamics

    Tetsuro Nagai, George A. Pantelopulos, Takuya Takahashi and John E. Straub

    Version of Record online: 24 JUN 2016 | DOI: 10.1002/jcc.24430

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    Simulated tempering (ST) is a commonly practiced generalized-ensemble algorithm that combines simulations at multiple temperatures. To perform efficient and stable ST simulation using molecular dynamics, the mass-scaling ST method is presented. The proper mass scaling employed in the new method makes the velocity distributions independent of temperature. This homogeneity enables simple temperature updates and makes the tempering simulation stable at high temperatures. The MSST method is applied to the folding of the Trpcage peptide over a wide range of temperature to demonstrate the gain in numerical stability.

  3. Quantum chemical study of the autoxidation of ascorbate (pages 1914–1923)

    Nils Herrmann, Norah Heinz, Michael Dolg and Xiaoyan Cao

    Version of Record online: 18 JUN 2016 | DOI: 10.1002/jcc.24408

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    The role of various ascorbate species in oxidation and autoxidation processes has been studied by quantum chemical methods. The necessity of wheather or not ascorbate autoxidations have to be catalyzed was evaluated by quantum chemical calculations. Ionization potentials and electron affinites have been calculated to evaluate the favored oxidizing and reducing agents among the ascorbate species. Furthermore calibration of density functional results was accomplished by single-point calculations of wavefunction-based theory methods.

  4. Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide-and-conquer, density-functional tight-binding, and massively parallel computation

    Hiroaki Nishizawa, Yoshifumi Nishimura, Masato Kobayashi, Stephan Irle and Hiromi Nakai

    Version of Record online: 18 JUN 2016 | DOI: 10.1002/jcc.24419

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    The linear-scaling divide-and-conquer (DC) quantum chemical methodology is applied to the density-functional tight-binding (DFTB) theory to develop a massively parallel program called DC-DFTB-K that can be routinely applied to on-the-fly molecular reaction dynamics simulations of large systems. Numerical tests based on calculations of water clusters in a cubic box show a single-point energy gradient calculation of a one-million-atom system is completed within 60 s using 7290 nodes of the K computer.

  5. A critical assessment of hidden markov model sub-optimal sampling strategies applied to the generation of peptide 3D models

    A. Lamiable, P. Thevenet and P. Tufféry

    Version of Record online: 18 JUN 2016 | DOI: 10.1002/jcc.24422

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    Hidden Markov Model derived structural alphabets are a probabilistic framework in which peptide conformations are described in terms of probability distributions. Here, strategies to sample suboptimal conformations are found to lead to the efficient generation of peptide conformations. Such approaches are also found to be as efficient as former protocols, while being one order of magnitude faster, opening the door to the large scale de novo modeling of peptides and mini-proteins.

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