Artificial Intelligence Powered Identification of Potential Antidiabetic Compounds in Ficus religiosa

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 de rived from F icus 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 enz yme dipeptidyl peptidase - 4 (DPP - 4). Flavonoids and alkaloids have emerged as attractive phytochemicals due to their strong binding int eractions 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 fo undation now facilitates future experimental validation of natural product therapies tailored for diabetic management. Introduction Type 2 diabetes mellitus is a global metabolic illness characterized by persistent hyperglycemia due to impaired insulin secretion and resistance [1], [2] . T2DM constitutes a growing global health issue, significantly burdening healthcare systems economically [5] . Research in 2021 indicated that 537 million persons globally had diabetes, with projections estimating 643 million cases by 2030 [6] .