Design, virtual screening and in silico QSPR modeling for the development of new thiosemicarbazone‐based complexes

Abstract

Eighteen new thiosemicarbazone ligands and 30 new ligand‐based complexes were developed from quantitative structure‐property relationships (QSPR) methods. Stability constants (logβ₁₂) of complexes were calculated on QSPR models that were built by methods of multivariate linear regression (MLR) and artificial neural network (ANN). Six descriptors, including dipole, ⁵C, ⁴N, fw, xc3, and ka1 were discovered in the best QSPR_MLR model with the good statistical criteria: R²_train = 0.892, Q²_CV = 0.845, and SE = 0.900. Besides, the ANN model with architecture I(6)‐HL(3)‐O(1) was built from the descriptors of the MLR model with excellent results as R²_train = 0.958, Q²_CV = 0.966, and Q²_test = 0.980. Also, the models were externally validated on the other experimental dataset. Consequently, the resulting QSPR models could be applied to develop new complexes for chemically related fields.