Quantitative structure-electrochemistry relationship for substituted benzenoids using Levenberg-Marquardt artificial neural network

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• 作者：Hadi Noorizadeh (1)
  Abbas Farmany (2)
  
  1. Young Researchers & Elite Club ; Ilam Branch ; Islamic Azad University ; Ilam ; Iran
  2. Department of Chemistry ; Faculty of Sciences ; Hamedan Branch ; Islamic Azad University ; Hamedan ; Iran
  
• 关键词：benzenoids ; half ; wave potential ; QSPR ; genetic algorithm ; Levenberg ; Marquardt artificial neural network
• 刊名：Russian Journal of Electrochemistry
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：51
• 期：3
• 页码：249-257
• 全文大小：304 KB
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• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Electrochemistry
  Physical Chemistry
  Russian Library of Science
  
• 出版者：MAIK Nauka/Interperiodica distributed exclusively by Springer Science+Business Media LLC.
• ISSN：1608-3342

文摘

Quantitative structure-property relationship models correlating the half-wave potentials (E 1/2) of the benzenoids and its derivatives were developed using both linear and non-linear modelling approaches. Descriptors calculated from molecular structures alone were used to represent the E 1/2 of the benzenoids. A set of 36 compounds were selected and suitable sets of molecular descriptors were calculated. A genetic algorithm-partial least square (GA-PLS) method was used to select the most appropriate molecular descriptors whilst a linear, quantitative structure-property relationship model was developed; using the selected descriptors, a Levenberg-Marquardt artificial neural network (L-M ANN) was employed for the non-linear model development. The stability and prediction ability of models were validated using leave-group-out cross-validation, external test set and Y-randomization techniques. The described model does not parameters require experimental and potentially provides useful prediction for E 1/2 of new benzenoids derivatives.