Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors

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• 作者：Sisir Nandi (1)
  Alessandro Monesi (1)
  Viktor Drgan (1)
  Franci Merzel (2)
  Marjana Novi? (1)
  
• 关键词：Quantitative structure ; activation barrier relationships ; Quantum chemical structural descriptors ; HOMO LUMO Energy ; Diels ; Alder ligations ; Multivariate linear regression and artificial neural network modelling
• 刊名：Chemistry Central Journal
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：7
• 期：1
• 全文大小：549 KB
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• 作者单位：Sisir Nandi (1)
  Alessandro Monesi (1)
  Viktor Drgan (1)
  Franci Merzel (2)
  Marjana Novi? (1)
  
  1. Laboratory of Chemometrics, National Institute of Chemistry, Hajdrihova 19, Ljubljana, 1000, Slovenia
  2. Laboratory of Biomolecular Structure, National Institute of Chemistry, Hajdrihova 19, Ljubljana, 1000, Slovenia
  
• ISSN：1752-153X

文摘

Background In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Results Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. Conclusions A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.