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has been cited by the following article:

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

  AUTHORS: Fatogoma Diarrassouba, Mawa Koné, Kafoumba Bamba, Yafigui Traoré, Mamadou Guy-Richard Koné, Edja Florentin Assanvo

  KEYWORDS: Tetracyanoquinodimethane, First Reduction Potential, QSPR, Statistical Analysis

  JOURNAL NAME: Computational Chemistry, Vol.7 No.4, October 22, 2019

  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.