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In the present work, the stability constants log11 and the concentration of metal ion and thiosemicarbazone in the solutions of their complex were determined by using in silico models. The 2D, 3D, physicochemical and quantum descriptors of the 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 QSPROLR (with k of 6) involved descriptors k0, core-core repulsion, xp5, xch5, valence, and SHHBd. The quality of model QSPROLR had the statistical values: R2train = 0.898, R2adj = 0.889, Q2LOO = 0.846, MSE = 1.136, and Fstat = 91.348. The neural network model QSPRANN with architecture I(6)-HL(6)-O(1) had the statistical values: R2train = 0.9768, and Q2LOO = 0.8687. The predictability of QSPR models for the complexes of the test group turned out to be in good agreement with those from the experimental data in the literature.

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