Indian Journal of Science and Technology
DOI: 10.17485/ijst/2019/v12i25/145108
Year: 2019, Volume: 12, Issue: 25, Pages: 1-10
Original Article
Nguyen Minh Quang2,3, Phạm Nu Ngoc Han1, Nguyen Thi Ai Nhung2 and Pham Van Tat1*
1Faculty of Science and Engineering, Hoa Sen University, Ho Chi Minh City, Vietnam
2Department of Chemistry, University of Sciences, Hue University, Hue City, Vietnam
3Faculty of Chemical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
*Author for correspondence
Pham Van TatFaculty of Science and Engineering, Hoa Sen University, Ho Chi Minh City, Vietnam
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
Subscribe now for latest articles and news.
