Author(s): Elena Kovac, Juan Carlos Garcia, Maria Oliveira and Hiroshi Tanaka

Abstract: Moringa oleifera, a plant rich in bioactive compounds, has attracted considerable interest for its potential health benefits. Traditionally utilized in herbal medicine, Moringa's nutritional profile and therapeutic properties are being explored through modern computational methods. This research uses machine learning techniques to analyze the bioactive compounds of Moringa oleifera, identifying key molecules responsible for its health-promoting effects. A comprehensive dataset was constructed, integrating molecular features of Moringa compounds, such as flavonoids, phenolics, and alkaloids, with their respective biological activities. Machine learning models, including support vector machines (SVM) and random forests, were employed to predict the bioactivity of these compounds and assess their pharmacological properties. The results indicate significant predictive power in determining the compounds most likely to exhibit therapeutic benefits, particularly in areas such as anti-inflammatory, antioxidant, and antimicrobial activities. The research further explores the correlation between chemical structure and biological function, offering insights into the molecular mechanisms that underpin Moringa's medicinal uses. This computational approach provides a robust framework for accelerating the discovery of novel bioactive compounds and enhancing our understanding of Moringa's role in health. These findings can contribute to the development of more effective Moringa-based therapeutic interventions, as well as guide future research into plant-based medicines using machine learning methodologies.

DOI: 10.33545/26633582.2025.v7.i2d.236

Pages: 335-338 | Views: 66 | Downloads: 30

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