IN SILICO MODEL QSPR FOR PREDICTION OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES

Abstract

In the present work, the stability constants logb₁₁ and the concentration of metal ion and thiosemicarbazone in complex solutions were determined by using in silico models. The 2D, 3D, physicochemical and quantum descriptors of complexes were generated from the molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The quantitative structure and property relationships (QSPRs) were constructed by using the ordinary linear regression (OLR) and artificial neural network (ANN). The best linear model QSPR_OLR (with k of 6) involved descriptors k0, core-core repulsion, xp5, xch5, valence, and SHHBd. The quality of model QSPR_OLR had the statistical values: R²_train = 0.898, R²_adj = 0.889, Q²_LOO = 0.846, MSE = 1.136, and F_stat = 91.348. The neural network model QSPR_ANN with architecture I(6)-HL(6)-O(1) had the statistical values: R²_train = 0.9768, and Q²_LOO = 0.8687. The predictability of QSPR models for complexes of the test group turned out to be in good agreement with those from the experimental data in the literature.