Abstract

Malaria, a disease caused by one of the world’s fatal parasites Plasmodium falciparum, is responsible for over a million death annually. P. falciparum dihydroorotate dehydrogenase (PfDHODH) is a validated target of this deadly parasite. Quantitative structure–activity relationship and molecular docking in silico methods were employed in the discovery of unique PfDHODH inhibitors from the computational design derivatives of indolyl-3-ethanone-α-thioethers through models generation via a genetic function algorithm methods. The best model indicates good power of prediction with coefficient of determination, R2 = 0.9482, adjusted coefficient of determination ({text{R}}_{text{adj}}^{2}) = 0.9288, Leave one out cross-validation coefficient (Q2) = 0.9201 and the external validation ({text{R}}_{text{pred}}^{2}) = 0.6467. The contribution of every descriptor in the model was investigated through finding their mean effect to (pIC50) the activities of the compounds. With MATS5m (− 0.11725), RDF75m (− 0.12097), VE3_Dzp (0.14697), and MLFER_BH (1.08528) contributing more to the model, while AATSC8p (− 0.04833) and minHBa (0.05430) contributed the least to the model. Hence, the mean effect indicated MLFER_BH to be the most relevant descriptor, which aided the design of five derivatives of indolyl-3-ethanone-α-thioethers. All the designed antimalarial compounds were deeply docked within the binding region thereby forming several hydrogens and hydrophobic bonds leading to the generation of better binding affinity and high binding scores (− 156.181 kcal/mol) compared to the design template (− 138.201 kcal/mol) and the standard drug (− 128.467 kcal/mol). Furthermore, all the five designed antimalarial compounds were found to be better bonded to the binding pocket of PfDHODH than other compounds reported by other researchers.

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