Artificial Intelligence Powered Identification of Potential Antidiabetic Compounds in Ficus religiosa

TL;DR

This study uses AI-driven computational methods to identify potential antidiabetic compounds from Ficus religiosa, highlighting flavonoids and alkaloids as promising candidates for further experimental validation.

Contribution

It introduces an integrated AI-based computational framework combining machine learning, molecular docking, and ADMET prediction for screening plant-derived antidiabetic agents.

Findings

Flavonoids and alkaloids show strong binding to DPP-4 enzyme.

AI methods accelerate screening and improve accuracy.

The approach supports future experimental validation of natural antidiabetic compounds.

Abstract

Diabetes mellitus is a chronic metabolic disorder that necessitates novel therapeutic innovations due to its gradual progression and the onset of various metabolic complications. Research indicates that Ficus religiosa is a conventional medicinal plant that generates bioactive phytochemicals with potential antidiabetic properties. The investigation employs ecosystem-based computational approaches utilizing artificial intelligence to investigate and evaluate compounds derived from Ficus religiosa that exhibit antidiabetic properties. A comprehensive computational procedure incorporated machine learning methodologies, molecular docking techniques, and ADMET prediction systems to assess phytochemical efficacy against the significant antidiabetic enzyme dipeptidyl peptidase-4 (DPP-4). DeepBindGCN and the AutoDock software facilitated the investigation of binding interactions via deep learning technology. Flavonoids and alkaloids have emerged as attractive phytochemicals due to their strong binding interactions and advantageous pharmacological effects, as indicated by the study. The introduction of AI accelerated screening procedures and enhanced accuracy rates, demonstrating its efficacy in researching plant-based antidiabetic agents. The scientific foundation now facilitates future experimental validation of natural product therapies tailored for diabetic management.