• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2019, Volume: 12, Issue: 25, Pages: 1-10

Original Article

Calculation of Stability Constant of Metal-thiosemicarbazone Complexes using MLR, PCR and ANN


Objectives: 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.

Keywords: Artificial Neural Network, Multivariate Linear Regression, Principle Component Regression, QSPR Models, Stability Constants logβ11, Thiosemicarbazone 


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