Antihypertensive Activity Prediction of Physalis angulata L. Through Computational Analysis

Abstract

Traditional medicine is widely used in Indonesia to improve health, but research into the safety and efficacy of medicinal plants has not been widely conducted. This study focuses on Physalis angulata L., commonly used in Southeast Asia, to explore its potential as an Angiotensin-Converting Enzyme (ACE) and Beta-1 Adrenergic Receptor (ADRB1) protein inhibitor. Utilizing a machine learning approach, compounds from Physalis angulata L. were identified and analyzed through in silico methods, with the AdaBoost Classifier model proving most effective. The identified active compounds, particularly Physanolide A, demonstrated superior binding affinity compared to reference ligands. Further analysis using Lipinski's rule of five confirmed the compliance and potential efficacy of Physanolide A and other compounds. While some compounds exhibited one violation of Lipinski's rule, they still presented promising binding interactions. This study emphasizes the complexity of protein-ligand interactions and the alternative binding strategies employed by natural compounds. Despite the limitations of in silico methods, the findings provide valuable insights into the potential of natural products as drug candidates. The study highlights the need for further in vitro and in vivo validation to ensure the safety and efficacy of these compounds. By addressing these limitations, this research contributes to the development of evidence-based natural therapies and a deeper understanding of protein-ligand interactions, paving the way for future investigations into the medicinal properties of Physalis angulata L.