Open AccessQSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors
Author(s) -
Hanine Hadni,
Mohamed Mazigh,
Menana El Hallaoui
Publication year - 2019
Publication title -
mediterranean journal of chemistry
Language(s) - English
Resource type - Journals
ISSN - 2028-3997
DOI - 10.13171/mjc8219040407hh
Subject(s) - chemistry , loo , quantitative structure–activity relationship , triazine , docking (animal) , cross validation , molecular descriptor , quantum chemical , linear regression , computational chemistry , stereochemistry , molecule , computational biology , artificial intelligence , machine learning , organic chemistry , medicine , nursing , computer science , biology
A quantitative structure-activity relationship (QSAR) investigation was performed towards 41 hybrids of 4-anilinoquinoline-triazines as potential antimalarial agents. The study was carried out using descendant multiple linear regression analyses (MLR), and artificial neural networks (ANN). Quantum chemical descriptors were calculated using DFT-B3LYP method, with the basis set 6-31G. The values obtained for the correlation coefficient of 0.87 and 0.92 by MLR and ANN, respectively, show a good predictive quality of the established model. In addition, the predicted model has been confirmed by several validation methods such as leave-one-out (LOO) cross-validation, Y-randomization, and external validation. The observed activity and the structural features of the studied molecules were further highlighted by molecular docking study on both wild and quadruple mutant type of pf-DHFR protein. Furthermore, the present work deals to study the binding modes and the key protein-ligand interactions. This methodology will be used to design new antimalarial drugs.