pamrmdsibcocapfcp2D QSAR, ADMET prediction and multiple receptor molecular docking strategy in bioactive compounds of Gracilaria corticata against Plasmodium falciparum (contractile protein)

Abstract

Background: Delivering a safer drug is a challenge for medicinal chemistry. In this study, bioactive compounds of Gracilaria corticata were screened by predicting their drug-like properties such as solubility, permeability, efficacy, metabolic stability, and toxicity. The potential drug optimization against virulent enzymes was calculated by docking algorithm using AutoDock 4.2.3. Molecular docking analysis reveals that the compounds are insoluble and impermeable, with better potential inhibition against virulent enzymes. Materials and methods: The bioactive compounds of Gracilaria corticata were screened for drug likeliness using the Lipinski rule of five and ADMET prediction. The structurally based docking analysis was done between organic compounds of plants against virulent proteins that are mainly responsible for causing disease. The interaction of rigid structure docked compounds was visualized using Discovery Studio. The QSAR studies of the compounds that have high binding energy against virulent enzymes were studied. Results: The structurally based drug screening of bioactive compounds resulted in better drug properties with controlled lipophilicity level, without causing toxicity that harms the natural habitat of humans. The compound Mono (2-Ethylhexyl) phthalate has the highest binding energy of −8.73 kcal/mol followed by 2-ethylhexyl isohexyl ester −7.73 kcal/mol against virulent enzymes. The QSAR studies of Mono (2-Ethylhexyl) phthalate were done to show the relationship between the set of atoms with correlative factors. Hence, molecular docking and in silico ADMET studies play a major role in improving prediction of drug compounds, and these compounds are able to act as potential inhibitors against contractile proteins of Plasmodium falciparum. Keywords: Gracilaria corticata, ADMET prediction, Contractile proteins, Molecular docking, In silico, QSAR