Review Article

From Leaf to Laboratory: Validating Tribal  Ethnomedicinal Wisdom through In Silico Science

Mohammad Nadeem khan.1 Praveen Chandra Dubey.2 Sanjay Vyas.3 Shushil Upadhayay.4 Arjun Prasad Tiwari.5 

• .1Department of Pharmacology (Clinical Pharmacology), Sri Aurobindo Medical College and PG Institute, Sri Aurobindo University, Indore, India 
• .2Ex-Principal Chief Conservator of Forests (PCCF), Government of Madhya Pradesh, India 
• .3Department of Microbiology and Horticulture, Govt. Holkar Science College, Indore, India 
• .4Project Coordinator, CSR/CER Activity, Traditional Healer, Sri Aurobindo University (Indore), SFRI, Jabalpur, Madhya Pradesh, India 
• .5National history museums, Mysore, Karnataka
•  

Correspondence: Mohammad Nadeem khan, Department of Pharmacology (Clinical Pharmacology) Sri Aurobindo Medical College & PG Institute, Sri Aurobindo University, Indore, Madhya Pradesh, India-453555, Email: [email protected]

Received: December 31, 2025                                                         Published: January 15, 2026

Citation: Nadeem khan M. From Leaf to Laboratory: Validating Tribal Ethnomedicinal Wisdom through In Silico Science. Int J Bio and Med. 2026;1(1):13–18. 

Copyright: ©2026 Nadeem khan M. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non commercially.

Abstract 

Background 

Traditional ethnomedicinal knowledge preserved by tribal communities represents an invaluable yet underexplored resource for modern drug discovery. However, the lack of systematic scientific validation and ethical integration has limited its translational impact in contemporary biology and medicine. Objective This manuscript presents an integrative “leaf-to-laboratory” framework that scientifically validates tribal ethnomedicinal wisdom using advanced in silico methodologies, while ensuring sustainability, ethical compliance, and translational relevance. 

Methods 

Ethnomedicinally significant plant species traditionally used by tribes of the Narmada Valley (Central India) were systematically evaluated through a multi-tiered in silico pipeline. Phytochemical libraries were curated and subjected to molecular docking, receptor–ligand interaction analysis, and structure–activity relationship assessment using state-of-the-art computational platforms. Predicted pharmacokinetic and toxicity profiles were further assessed to prioritize lead compounds. The overall workflow and mechanistic insights are illustrated schematically in Figure 1, while Figure 2 summarizes receptor-specific interaction networks and therapeutic targeting strategies. 

Results 

Computational screening revealed several bioactive phytochemicals with high binding affinity toward clinically relevant molecular targets, including opioid, GABAergic, and hypoxia-responsive pathways. These findings provide molecular-level validation for the traditional use of plants such as Mitragyna speciosa, Garuga pinnata, and Bauhinia spp. The in silico prioritization strategy significantly reduced experimental burden while enhancing predictive accuracy, thereby offering a sustainable alternative to conventional resource-intensive pharmacological screening. 

Innovation and Significance 

This study advances an ethically grounded, eco-conscious, and technologically robust paradigm that bridges indigenous knowledge systems with modern computational biology. By aligning in silico drug discovery with the principles of the Nagoya Protocol, the proposed approach safeguards biodiversity, prevents biopiracy, and promotes equitable benefit sharing. 

Conclusion 

The integration of tribal ethnomedicine with advanced in silico validation represents a transformative strategy for sustainable and inclusive biomedical innovation. This framework not only accelerates natural-product-based drug discovery but also repositions indigenous knowledge as a scientifically credible and ethically protected contributor to future therapeutic development. 

Keywords: Ethnobotany; In-Silico Validation; Tribal Medicine; Computational Drug Discovery; Biodiversity Conservation; Nagoya Protocol
 

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