An insight QSPR‐based prediction model for stability constants of metal‐thiosemicarbazone complexes using MLR and ANN methods. Issue 4 (23rd August 2019)

• Record Type: Journal Article
• Authors: Quang, Nguyen Minh; Nhung, Nguyen Thi Ai; Van Tat, Pham
• Is Part Of: Vietnam journal of chemistry. Volume 57:Issue 4(2019)
• Page Start: 500
• Page End: 506
• Publication Date: 2019-08-23
• Subjects: QSPR models -- complexes of thiosemicarbazones -- stability constants logβ12 -- multivariate linear regression -- artificial neural network; Chemistry -- Periodicals; 540
• DOI: 10.1002/vjch.201900070
• Languages: English
• ISSNs: 0866-7144

Abstract

Abstract: In the present investigation, the stability constants (log β 12 ) of complexes (ML2 ) between metal ions (M) and thiosemicarbazones (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 (R 2 train ) and prediction (Q 2 LOO ) determination coefficient 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 (Q 2 test ) for external predictivity with the QSPRMLR and QSPRANN value of 0.8321 and 0.8953, respectively. A series of new metal‐thiosemicarbazones complexes were designed based on the descriptor of the models and predicted the stability constants of the complexes.