Calculation of stability constant of metal-thiosemicarbazone complexes using MLR, PCR and ANN

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.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.identifier.issn0974-5645
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/10928
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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