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QSAR and molecular docking modelling of anti-leishmanial activities of organic selenium and tellurium compounds.

Authors :
Cabrera N
Mora JR
Márquez E
Flores-Morales V
Calle L
Cortés E
Source :
SAR and QSAR in environmental research [SAR QSAR Environ Res] 2021 Jan; Vol. 32 (1), pp. 29-50. Date of Electronic Publication: 2020 Nov 26.
Publication Year :
2021

Abstract

Leishmaniasis affects mainly rural areas and the poorest people in the world. A computational study of the antileishmanial activity of organic selenium and tellurium compounds was performed. The 3D structures of the compounds were optimized at the wb97xd/lanl2dz level and used in the quantitative structure-activity relationship (QSAR) analysis. The antileishmanial activity was measured by L. donovani β carbonic anhydrase inhibition (Ki) and the half-maximal inhibitory concentration (IC <subscript>50</subscript> ) against L. infantum amastigotes. The dataset was divided into training (75%) and test sets (25%) by using a k-means clustering algorithm. For pKi prediction, model M3 with seven 3D topographic descriptors was characterized by the following statistical parameters: r <superscript>2</superscript>  = 0.879, Q <superscript>2</superscript> <subscript>LOO</subscript>  = 0.822, and Q <superscript>2</superscript> <subscript>ext</subscript>  = 0.840. For pIC <subscript>50</subscript> prediction, model M12 with six attributes was characterized by the following statistical parameters: r <superscript>2</superscript>  = 0.907, Q <superscript>2</superscript> <subscript>LOO</subscript>  = 0.824, and Q <superscript>2</superscript> <subscript>ext</subscript>  = 0.795. Both models met all the requirements of Tropsha´s test, which implies predictions of pIC <subscript>50</subscript> and pKi activities with high accuracy. Concomitantly, favourable interactions of the sulphonamide group with the Zn atom in the protein were revealed by the docking analysis.

Details

Language :
English
ISSN :
1029-046X
Volume :
32
Issue :
1
Database :
MEDLINE
Journal :
SAR and QSAR in environmental research
Publication Type :
Academic Journal
Accession number :
33241943
Full Text :
https://doi.org/10.1080/1062936X.2020.1848914