Pham, Van TatNguyen, Minh QuangPham, Nu Ngoc HanNguyen, Thi Ai Nhung20190974-5645https://thuvienso.hoasen.edu.vn/handle/123456789/10928Objectives: In this work, the stability constants log β11 of complexes between thiosemicarbazone and metal ions were predicted based on the modeling of Quantitative Structure and Property Relationship (QSPR). Methods: The QSPR models have been developed by using Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Artificial Neural Network (ANN). Findings: The results of QSPR models building have provided very positive results through the statistical values of validation. The QSPR models were cross-validated based on critical statistics. The quality of the QSPR models was exhibited by the statistical standards as the QSPRMLR model: R2 train = 0.9446, R2 adj = 0.939, Q2 LOO = 0.9262, SE = 0.529 and Fstat = 160.817; QSPRPCR model: R2 train = 0.949, R2 adj = 0.942, Q2 CV = 0.928, MSE = 0.292, RMSE = 0.540 and Fstat = 134.617; QSPRANN model with architecture I (7)-HL(10)-O(1): R2 train = 0.986, Q2 CV = 0.984 and R2 test = 0.983. Applications: Obviously, the results from this work could serve for designing new thiosemicarbazone derivatives that are helpful in the fields of analytical chemistry, pharmacy and environment.10 p.enArtificial neural networkMultivariate linear regressionPrinciple component regressionQSPR modelsArticle