Volume: 47 Issue: 2
Synergistic integration of bioinformatics and Ayurveda in obesity and cancer management: Advancements, challenges and future directions
Year: 2025, Page: 58-70, Doi: https://doi.org/10.62029/jmaps.v47i2.jaiswal
Received: April 30, 2025 Accepted: Nov. 12, 2025 Published: Nov. 30, 2025
Obesity is a global health concern linked not only to metabolic disorders but also to several types of cancers. Ayurvedic medicine, an ancient Indian healthcare tradition, presents a wealth of phytoconstituents with potential therapeutic benefits for obesity and related cancers. Recent advances in bioinformatics provide powerful methods for identifying and validating these natural molecules. This review emphasizes the synergistic integration of Ayurveda and bioinformatics, focusing on phytoconstituents such as flavonoids and coumarins, computationally validated as potent pancreatic lipase inhibitors. Virtual screening, molecular docking, QSAR modelling, pharmacophore mapping, and molecular dynamics simulations were employed to predict, validate, and optimise these candidates. Network pharmacology further revealed multitarget potential against obesity-linked cancer pathways involving genes like CXCL12 and LEP. Despite these promising advancements, significant challenges remain, including the chemical complexity of Ayurvedic formulations, limited bioavailability, insufficient translation from computational predictions to experimental validation, and interdisciplinary communication gaps. Addressing these issues through improved databases, refined computational methods, and collaborative frameworks can fully unlock the therapeutic potential of Ayurveda, redefined through the lens of bioinformatics.
Keywords: Ayurvedic, Bioinformatics, Cancer, MD simulation, Obesity.
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Jaiswal, S. V., Upadhyay, K. K., Modanwal, S., & Mishra, N. (2025). Synergistic integration of bioinformatics and Ayurveda in obesity and cancer management: Advancements, challenges and future directions. Journal of Medicinal and Aromatic Plant Sciences, 47(2), 58–70. https://doi.org/10.62029/jmaps.v47i2.jaiswal
