Quantitative Structure Activity Relationship (QSAR) and Molecular Docking Study of Some Pyrrolones Antimalarial Agents against Plasmodium Falciparum

Abstract

The increase in multidrug resistance malaria cases necessitates the need to search for new cost effective drugs. QSAR and molecular docking studies were performed on a data set of forty nine Pyrrolones antimalarial agents against Plasmodium falciparum. Forty two molecules were used as training set and seven as test set. The molecular descriptors were obtained by Density Functional Theory (DFT) (B3LYP/6-31G**) level of calculation. The QSAR model was built using Genetic Function Algorithm (GFA) method. The model with the best statistical significance (N = 42, R²_ext = 0.700, R² = 0.933, R²a = 0.916, Q²cv = 0.894, LOF = 0.417, Min expt. error for non-significant LOF (95%) = 0.250 was selected. The docking experiment was carried out using AutoDock Vina of PyRx and Discovery Studio Visualizer. Docking analysis revealed that three of the studied compounds with binding affinity values of -10.7 kcal/mol, -10.9 kcal/mol and -11.1 kcal/mol possess higher potency than standard antimalarial drugs with binding affinity of values of -8.8 kcal/mol, -9.5 kcal/mol and -9.0 kcal/mol. It is envisioned that the wealth of information provided by the QSAR and molecular docking results in this study will offer important structural insights for further laboratory experiments in the future design of novel and highly potent antimalarial from Pyrrolones.

Keywords

• : Antimalarial agents,Density Functional Theory,Genetic Function Algorithm,Discovery Studio Visualizer

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