Journal of Computer Aided Chemistry
Online ISSN : 1345-8647
ISSN-L : 1345-8647
Volume 16
Showing 1-3 articles out of 3 articles from the selected issue
  • Shohei Sanada, Takaaki Kuroda, Michinori Sumimoto, Kenji Hori
    2015 Volume 16 Pages 30-38
    Published: 2015
    Released: November 27, 2015
    The palladium-catalyzed Mizoroki-Heck reaction is one of the most widely used and important reactions in organic synthesis and organometallic chemistry. Since the ligand of Pd complex used as a catalyst influences a reaction, the search for ligands is one of the important work. In this study, we theoretically investigated the reaction mechanism of Mizoroki-Heck reaction with PdCl2(2-(2'-pyridyl) benzo azole) catalyst using the DFT method. From the calculated results, the active species was found to be Pd(Ph)(Br)(L) complex which produced by the reactive substrates. There are consisted of seven processes in this reaction mechanism, as follow: (1) ethylene coordination, (2) ethylene Insertion, (3) β-H Abstraction, (4) styrene elimination, (5) Ph-Br coordination, (6) H-Br elimination, and (7) Ph-Br oxidative addition. The rate-determining step is Ph-Br oxidative addition, and the values of the activation free energies were calculated to be 30-32 kcal/mol. From these results, it was suggested that this catalytic cycle is proceeded by Pd0/PdII, not PdII/PdIV.
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  • Norihito Kawashita, Hiroyuki Yamasaki, Tomoyuki Miyao, Kentaro Kawai, ...
    2015 Volume 16 Pages 15-29
    Published: 2015
    Released: October 29, 2015
    We have reviewed chemoinformatics approaches for drug discovery such as aromatic interactions, aromatic clusters, structure generation, virtual screening, de novo design, evolutionary algorithm, inverse-QSPR/QSAR, Monte Carlo, molecular dynamics, fragment molecular orbital method and matched molecular pair analysis from the viewpoint of young researchers. We intend to introduce various fields of chemoinformatics for non-expert researchers. The structure of this review is given as follows: 1. Introduction, 2. Analysis of Aromatic Interactions, 2.1 Aromatic Interactions, 2.2 Aromatic Clusters, 3. Ligand Based Structure Generation, 3.1 Virtual Screening, 3.2 De Novo Ligand Design, 3.3 Combinatorial Explosion, 3.4 Inverse-QSPR/QSAR, 4. Trends in Chemoinformatics-Based De Novo Drug Design, 5. Conformational Search Method Using Genetic Crossover for Bimolecular Systems, 6. Interaction Analysis using Fragment Molecular Orbital Method for Drug Discovery, 7. Matched Molecular Pair Analysis and SAR Analysis by Fragment Molecular Orbital Method, 8. Chemoinformatics Approach in Pharmaceutical Processes, 9. Conclusion.
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  • Kenichi Tanaka, Hiromasa Kaneko, Kyosuke Nagasaka, Kimito Funatsu
    2015 Volume 16 Pages 1-14
    Published: 2015
    Released: August 06, 2015
    Supplementary material
    Soft sensors have been widely used in chemical processes to predict values of difficult-to-measure process variables online. If the relationship between explanatory variables X and an objective variable y is changed by catalyst deterioration, change of product and so on, prediction accuracy of a soft sensor is reduced. This problem is called degradation of a soft sensor model. To overcome the degradation, many adaptive soft sensors have been proposed. In this paper, we aim to improve prediction accuracy of just-in-time (JIT) models. JIT models are constructed with only data close to a query or with all data having weights according to similarity with a query. If the type of degradation is shift of y-value, prediction accuracy of JIT models is reduced since data with similar X-values but different y-values are mixed in database and the relationship between X and y is not consistent. To resolve this problem, we propose to update database based on not only X-distance but also y-distance. The updated database is called JIT database. When a y-value is measured and a datum of X and y is obtained, data whose X-distance is low and y-distance is high from the datum are moved from JIT database to original database, and data whose X-distance and y-distance are both low are moved from original database to JIT database. To evaluate the performance of the proposed method, we used fifteen types of simulation data containing five types of state transition (Y-shift, X-shift, Slope-change, Y-shift + Slope-change and X-shift + Slope-change), and three types of transition speed (Instant, Rapid and Gradual). By using the proposed method, improvement of prediction accuracy of JIT models was achieved for all types of simulation data.
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