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In this study, the stability constants (logβ11) of twenty-eight new complexes between several ion metals and thiosemicarbazone ligands were predicted on the basis of the quantitative structure property relationship (QSPR) modeling. The stability constants were calculated from the results of the QSPR models. The QSPR models were built by using the multivariate least regression (QSPRMLR) and artificial neural network (QSPRANN). The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The best linear model QSPRMLR involves five descriptors, namely Total energy, xch6, xp10, SdsN, and Maxneg. The quality of the QSPRMLR model was validated by the statistical values that were R2train = 0.860, Q2LOO = 0.799, SE = 1.242, Fstat = 54.14 and PRESS = 97.46. The neural network model QSPRANN with architecture I(5)-HL(9)-O(1) was presented with the statistical values: R2train = 0.8322, Q2CV = 0.9935 and Q2test = 0.9105. Also, the QSPR models were evaluated externally and achieved good performance results with those from the experimental literature. In addition, the results from the QSPR models could be used to predict the stability constants of other new metal-thiosemicarbazones.

Currently, many drugs are being studied and potentially used in the treatment of SARS-CoV-2. Compounds studied are mostly organic substances. This work investigates the ability to inhibit SARS-CoV-2 of various 20 metal ions based on their ability to inhibit several biological systems; the physicochemical properties of metal ions were calculated by quantum chemistry DFT (B3LYP/ LanL2DZ) were used to develop the QIPAR hybrid models. Hybrid models QIPARGA-MLR (k = 4) and QIPARGA-ANN with architecture I(4)-HL(9)-O(1) were developed to predict the biological activity of metal ions. Metal ions were also investigated for their inhibitory potential for the protein SARS-CoV-2 (PDB6LU7) by docking simulation techniques. We predicted the binding sites of metal ions to the active sites of the SARS-CoV-2 protein (PDB6LU7). These studies are consistent with their activities against different biological systems. This research will also contribute to the development of metal oxide nanomaterials.