A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method

Keshavarz, M. H.; Shirazi, Zeinab; Barghahi, Asileh; Mousaviazar, Ali; Zali, Abbas

A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method

2022

Keshavarz, M. H. (Mohammad Hossein) | Shirazi, Zeinab | Barghahi, Asileh | Mousaviazar, Ali | Zali, Abbas

A novel model is presented for reliable estimation of the stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions (log β₁₁) in an aqueous solution, which has wide usage in environmental safety and ecotoxicology applications. The biggest reported data of log β₁₁ for 120 metalthiosemicarbazone complexes are used for deriving and testing the novel model. In contrast to available methods where they need the two-dimensional (2D) and three-dimensional (3D) complex molecular descriptors as well as expert users and computer codes, the novel correlation uses four additive and two non-additive structural parameters of thiosemicarbazone ligands. The calculated results of the novel correlation are compared with the outputs of the genetic algorithm with multivariate linear regression method (GA-MLR) as one of the best existing methods, which requires seven complex descriptors. The estimated results for 78 of training as well as 42 of two different test sets were established by external and internal validations. The values of statistical parameters comprising average deviation, average absolute deviation, average absolute relative deviation, absolute maximum deviation, and the coefficient of determination for 73 data of training set of New model/GA-MLR are 0.04/ − 0.25, 1.06/1.31, 14.4/18.7, 3.18/7.92, and 0.830/0.652, respectively. Thus, the predicted results of the new model are worthy as compared to the complex GA-MLR model. Moreover, assessments of various statistical parameters confirm that the new model provides great reliability, goodness-of-fit, accuracy, and precision.

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