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Calculation of stability constants of new metal-thiosemicarbazone complexes based on the QSPR modeling using MLR and ANN methods
Calculation of stability constants of new metal-thiosemicarbazone complexes based on the QSPR modeling using MLR and ANN methods
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Date
2021
Authors
Quang Nguyen Minh
An Tran Nguyen Minh
Tat Pham Van
Thuy Bui Thi Phuong
Duoc Nguyen Thanh
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Abstract
In this study, the stability constants (log 11) of twenty-eight new complexes between several ion
metals and thiosemicarbazone ligands were predicted on the basis of the quantitative structure property
relationship (QSPR) modeling. The stability constants were calculated from the results of the QSPR
models. The QSPR models were built by using the multivariate least regression (QSPRMLR) and artificial
neural network (QSPRANN). The molecular descriptors, physicochemical and quantum descriptors of
complexes were generated from molecular geometric structure and semi-empirical quantum calculation
PM7 and PM7/sparkle. The best linear model QSPRMLR involves five descriptors, namely Total energy,
xch6, xp10, SdsN, and Maxneg. The quality of the QSPRMLR model was validated by the statistical
values that were R2
train = 0.860, Q2
LOO = 0.799, SE = 1.242, Fstat = 54.14 and PRESS = 97.46. The
neural network model QSPRANN with architecture I(5)-HL(9)-O(1) was presented with the statistical
values: R2
train = 0.8322, Q2
CV = 0.9935 and Q2
test = 0.9105. Also, the QSPR models were evaluated
externally and achieved good performance results with those from the experimental literature. In
addition, the results from the QSPR models could be used to predict the stability constants of other new
metal-thiosemicarbazones.
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Keywords
"Artificial neural network,
multivariate least regression,
QSPR,
stability constants log 11,
thiosemicarbazone."