Development of new metal-thiosemicarbazone complexes using visual screening methods and in silico models

Abstract

The stability constants (logb₁₁) of forty-two new metal-thiosemicarbazone complexes were predicted based on the results of the quantitative structure-property relationship (QSPR). The QSPR models were developed from 88 logb₁₁ values of experimental complexes by using the multivariate linear regression (QSPR_MLR) and artificial neural network (QSPR_ANN). Four descriptors such as xch9, xv0, core-core repulsion and cosmo area were found out in the best of the linear model QSPR_MLR which was harshly evaluated by the statistical values: R²_train = 0.864, Q²_LOO = 0.840, SE = 0.711, F_stat = 131,355 and PRESS = 49.31. Furthermore, the artificial neural network model QSPR_ANN with architecture I(4)-HL(5)-O(1) was discovered with the same variables of the QSPR_MLR model that the statistical results were extremely impressive as R²_train = 0.970, Q²_CV = 0.984 and Q²_test = 0.974. Also, both of the QSPR models were externally validated on the data set of 18 logb11 values of independently experimental complexes. As a consequence, the results from the QSPR models could be used to calculate the stability constants of other new metal-thiosemicarbazones.