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Development of Predictive QSPR Model of the First Reduction Potential from a Series of Tetracyanoquinodimethane (TCNQ) Molecules by the DFT (Density Functional Theory) Method

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  1. 1 Laboratoire de Thermodynamique et de Physico-chimie du Milieu, UFR-SFA, Université Nangui Abrogoua, Abidjan, Côte d’Ivoire
  2. 2 Laboratoire de Chimie Organique et des Substances Naturelles, UFR-SSMT, Université Félix Houphouët Boigny, Abidjan, Côte d’Ivoire
Computational Chemistry·Volume 07, Issue 4·Pages 121–142·Published 21 October 2019·DOI10.4236/cc.2019.74009
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Abstract

In this work, which consisted to develop a predictive QSPR (Quantitative Structure-Property Relationship) model of the first reduction potential, we were particularly interested in a series of forty molecules. These molecules have constituted our database. Here, thirty molecules were used for the training set and ten molecules were used for the test set. For the calculation of the descriptors, all molecules have been firstly optimized with a frequency calculation at B3LYP/6-31G(d,p) theory level. Using statistical analysis methods, a predictive QSPR (Quantitative Structure-Property Relationship) model of the first reduction potential dependent on electronic affinity (EA) only have been developed. The statistical and validation parameters derived from this model have been determined and found interesting. These different parameters and the realized statistical tests have revealed that this model is suitable for predicting the first reduction potential of future TCNQ (tetracyanoquinodimethane) of this same family belonging to its applicability domain with a 95% confidence level.

KeywordsTetracyanoquinodimethaneFirst Reduction PotentialQSPRStatistical Analysis
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