Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/23317
Title: An insight QSPR-based prediction model for stability constants of metal-thiosemicarbazone complexes using MLR and ANN methods
Authors: Nguyen, Minh Quang
Nguyen, Thi Ai Nhung
Pham, Van Lai
Keywords: QSPR models
Complexes of thiosemicarbazones
Stability constants logβ₁₂
Multivariate linear regression
Artificial neural network
Issue Date: 2019
Series/Report no.: Vietnam Journal of Chemistry;No 57(04) .- Page.500-506
Abstract: In the present investigation, the stability constants (logβ₁₂) of complexes (ML₂) between metal ions (M) and thiosenucarbazones (L) were used as an endpoint in the quantitative structure-property relationship (QSPR) approaches. The molecular descriptors of the experimental complexes were calculated from the conformation with the lowest binding free energy by means of semi-empirical PM7 method. QSPR models were developed by using multivariate linear regression (MLR) and artificial neural network methods (ANN). The best QSPR models found out three important descriptors as knotp, Cosmo Area and Hmin in the metal-thiosemicarbazones complexation. The final QSPRmlr model had shown satisfactory statistical performance; training(R² train) and prediction (Q² lod) determination coerticient of 0.9274 and 0.8784, respectively. Meanwhile, the statistical results of QSPRANN model received the value of 0.9844 and 0.9898. The models also ratified strict statistical validation tests (Q²-test) for external predictivity with the QSPRMI R and QSPRANN value of 0.8321 and 0.8953, respectively. A series of new metal-thiosemicarbazones complexes were designed based on the deseriptor of the models and predicted the stability constants of the complexes.
URI: http://dspace.ctu.edu.vn/jspui/handle/123456789/23317
ISSN: 2525-2321
Appears in Collections:Vietnam Journal of Chemistry

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