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STUDI IN SILICO AKTIVITAS SENYAWA EKSTRAK BUNGA TELANG (Clitoria ternatea L.) SEBAGAI AGEN ANTIHIPERTENSI DENGAN PENDEKATAN NETWORK PHARMACOLOGY DAN MOLECULAR DOCKING
Hypertension is one of the major risk factors for cardiovascular disease worldwide that contributes to increased morbidity and mortality rates. If not treated properly, hypertension can cause serious complications in vital organs such as the heart, kidneys, brain, and eyes. The search for new antihypertensive agents is very important and one approach that can be taken is through the exploration of bioactive compounds from natural ingredients such as butterfly pea flowers (Clitoria ternatea L.). This study aims to evaluate the potential of potential compounds found in butterfly pea flowers as antihypertensive agents, through an in silico approach involving network pharmacology and molecular docking analysis. Identification of butterfly pea flower extract compounds was carried out using GC-MS. Various bioinformatics applications were applied to analyze the relationship between compounds and molecular targets. Identification of the main compounds was carried out through the PubChem and Swiss Target Prediction databases, prediction of molecular targets related to hypertension refers to GeneCards. Molecular interactions, network analysis between compounds and targets using STRING and STITCH were visualized using Cytoscape in identifying the main biological pathways. Molecular modeling was used using AutoDock Vina to evaluate the strength and stability of the binding of active compounds and target proteins such as ACE, AKT1, and NOS3 through hydrogen and hydrophobic bonds. The myo-inositol compound on the ACE receptor has a binding affinity value of -5.435 and RMSD 0 Å. Myo-inositol shows similar activity to the reference drug (captopril) making it a prime candidate as a potential compound for hypertensive agents in inhibiting enzymes and modulating molecular pathways that play a role in blood pressure regulation. Keywords: butterfly pea flower, hypertension, in silico, molecular prediction, network pharmacology
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2507003073 | T174001 | T1740012025 | Central Library (Reference) | Available but not for loan - Not for Loan |
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