Publication Date: 2023/12/04
Abstract: Malaria is a deadly disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. Of the five species of Plasmodium, P. falciparum is the deadliest, and findings have shown to have growing evidence of drug-resistance mechanisms in malaria treatments. Therefore, the identification of new drug targets is an urgent need for the clinical management of the disease. In this study, we employ an approach of identifying drug leads against fructose bisphosphate aldolase, a potent drug target in P. falciparum.Molecular docking was carried out using PyRx and CBDock to determine the binding affinities of protein-ligand complexes. Two drug leads were generated using machine learning. These drug leads were selected based on Lipinski’s drug-likeness criteria. The ligand 5-Chloro-1-(2-phenylethyl)-1H-indole-2,3- dione exerted the highest binding effect on the aldolase as compared to 1-(7,8 Dihydronaphthalen-2-ylmethyl)- 5-(piperidine-1-carbonyl)indole-2,3-dione using molecular docking. The 5-Chloro-1-(2-phenyl ethyl)-1H- indole-2,3-dione superior binding affinity with bisphosphate aldolase compared to 1-(7,8- Dihydronaphthalen-2-ylmethyl)-5-(piperidine-1- carbonyl)indole-2,3-dione imply that it can inhibit the bisphosphate aldolase activity in the plasmodium falciparum.
Keywords: BLAST; Molecular Docking; Machine Learning; QSAR; Drug Lead; Drug Target; Aldolase.
DOI: https://doi.org/10.5281/zenodo.10255193
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV1914.pdf
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