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dc.contributor.authorPham, Van Tat
dc.contributor.authorNguyen, Minh Quang
dc.contributor.authorPham, Nu Ngoc Han
dc.contributor.otherNguyen, Thi Ai Nhung
dc.date.issued2019
dc.identifier.issn0974-5645
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/10928
dc.description.abstractObjectives: 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.
dc.format10 p.
dc.language.isoen
dc.sourceIndian Journal of Science and Technology. Volume 12, No. 25
dc.subjectArtificial neural network
dc.subjectMultivariate linear regression
dc.subjectPrinciple component regression
dc.subjectQSPR models
dc.titleCalculation of stability constant of metal-thiosemicarbazone complexes using MLR, PCR and ANN
dc.typeArticle


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