Extraction, Isolation, Identification Tests For Poly Herbal Gel For Anti-Bacterial Using Artificial Intelligence

Abstract

The increasing prevalence of antimicrobial resistance has created an urgent need for safe, effective, and plant-based alternatives to conventional antibacterial therapies. The present study aims to develop and evaluate a polyherbal antibacterial gel formulated from Punarnava (Boerhavia diffusa), Uttareni (Achyranthes aspera), Bilwa (Aegle marmelos), and Nela Usiri (Phyllanthus niruri). Plant materials will be subjected to Soxhlet extraction using suitable solvents, followed by phytochemical screening, isolation, and identification of bioactive constituents. Artificial Intelligence (AI)-assisted tools will be employed for literature mining, prediction of phytochemical–target interactions, and prioritization of potential antibacterial compounds. Molecular docking and enzyme–substrate interaction studies will be performed to investigate the binding affinity of selected phytoconstituents against bacterial target proteins. The optimized extracts will be incorporated into a topical gel formulation, which will be evaluated for physicochemical properties, spreadability, viscosity, pH, homogeneity, stability, antimicrobial activity, and in vitro drug release using diffusion studies. The findings are expected to provide scientific evidence supporting the development of a stable, effective, and AI-assisted polyherbal antibacterial gel with potential applications in wound care and topical infection management.