Thiosemicarbazone Complexes of Uranium(IV)

Herein, we report the synthesis of eight new mononuclear and binuclear Co2+, Ni2+, Cu2+, and Zn2+ methoxy thiosemicarbazone (MTSC) complexes aiming at obtaining thiosemicarbazone complex with potent biological activity. The structure of the MTSC ligand and its metal complexes was fully characterized by elemental analysis, spectroscopic techniques (NMR, FTIR, UV-Vis), molar conductivity, thermogravimetric analysis (TG), and thermal differential analysis (DrTGA). The spectral and analytical data revealed that the obtained thiosemicarbazone-metal complexes have octahedral geometry around the metal center, except for the Zn2+-thiosemicarbazone complexes, which showed a tetrahedral geometry. The antibacterial and antifungal activities of the MTSC ligand and its (Co2+, Ni2+, Cu2+, and Zn2+) metal complexes were also investigated. Interestingly, the antibacterial activity of MTSC- metal complexes against examined bacteria was higher than that of the MTSC alone, which indicates that metal complexation improved the antibacterial activity of the parent ligand. Among different metal complexes, the MTSC- mono- and binuclear Cu2+ complexes showed significant antibacterial activity against Bacillus subtilis and Proteus vulgaris, better than that of the standard gentamycin drug. The in silico molecular docking study has revealed that the MTSC ligand could be a potential inhibitor for the oxidoreductase protein.

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Synthesis and Characterization of Glyoxalic Acid Thiosemicarbazone Complexes of Some Bivalent Metal Ions

Synthesis and Reactivity in Inorganic and Metal-Organic Chemistry ◽

10.1080/15533179508218230 ◽

1995 ◽

Vol 25 (3) ◽

pp. 417-427 ◽

Cited By ~ 21

Author(s):

Jun—Lian Tian ◽

En—Qing Gao ◽

Yan—Tuan Li ◽

Shu—Xiang Liu

Keyword(s):

Metal Ions ◽

Synthesis And Characterization ◽

Bivalent Metal ◽

Thiosemicarbazone Complexes ◽

Glyoxalic Acid ◽

Bivalent Metal Ions

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Tricarbonyl 99mTc(i) and Re(i)–thiosemicarbazone complexes: synthesis, characterization and biological evaluation for targeting bacterial infection

Dalton Transactions ◽

10.1039/c5dt02264a ◽

2015 ◽

Vol 44 (36) ◽

pp. 16136-16148 ◽

Cited By ~ 5

Author(s):

Dipak Kumar Nayak ◽

Rinku Baishya ◽

Ramalingam Natarajan ◽

Tuhinadri Sen ◽

Mita Chatterjee Debnath

Keyword(s):

Bacterial Infection ◽

Biological Evaluation ◽

Thiosemicarbazone Complexes

Tricarbonyl 99mTc(1) and Re(1) thiosemicarbazone complexes have been synthesised, characterised crystallographically and subjected to biological evaluation in rats intramuscularly infected with S. aureus.

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QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

Journal of Science and Technology - IUH ◽

10.46242/jst-iuh.v36i06.862 ◽

2021 ◽

Vol 36 (06) ◽

Author(s):

NGUYEN MINH QUANG ◽

TRAN NGUYEN MINH AN ◽

NGUYEN HOANG MINH ◽

TRAN XUAN MAU ◽

PHAM VAN TAT

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stability Constants ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Structure Property ◽

Least Square Regression ◽

Thiosemicarbazone Complexes ◽

Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. 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 QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

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