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Predictive Artificial Neural Network Model for Solvation Enthalpy of Organic Compounds in N,N-Dimethylformamide

Abstract

A quantitative structure-property relationship (QSPR) strategy was followed for prediction of the solvation enthalpy (ΔHsolv) values for 116 organic compounds in N,N-dimethylformamide. At first, a three-parameter multiple linear regression (MLR) model including the hydrophilic factor (Hy), sum of Kier–Hall electrotopological states (Ss) and 3D-MoRSE signal 15 weighted by atomic van der waals volumes (Mor15v) was generated for the enthalpy data. The model showed a standard error of 5.49 and R2 = 0.9220. The descriptors were then employed to develop an artificial neural network (ANN) model for estimating the solvation enthalpies. The developed ANN with 3-6-1 topology resulted in the R2 values of 0.9914, 0.9765, and 0.9796 for the training, validation and test sets, respectively. Relative importances of Ss, Mor15v and Hy were found to be 48.86, 32.08, and 19.06%, respectively. The findings proved the significant role of molecular topology, electron density and hydrophilicity as the structural features determining ΔHsolv values of the organic compounds in N,N-dimethylformamide.

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ACKNOWLEDGMENTS

Financial support from the Research Council of Islamic Azad University of Rasht branch is sincerely acknowledged.

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Correspondence to Fariba Safa.

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Fariba Safa, Dakhel, A.A. & Shariati, S. Predictive Artificial Neural Network Model for Solvation Enthalpy of Organic Compounds in N,N-Dimethylformamide. Russ. J. Phys. Chem. 93, 2661–2668 (2019). https://doi.org/10.1134/S0036024419130260

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Keywords:

  • quantitative structure–property relationship
  • enthalpy of solvation
  • multiple linear regression
  • artificial neural network
  • N,N-dimethylformamide