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 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.

ABBREVIATIONS

AI – Artificial Intelligence
T2DM – Type 2 Diabetes Mellitus
DPP-IV – Dipeptidyl Peptidase IV
GLP-1 – Glucagon-Like Peptide-1
GIP – Glucose-Dependent Insulinotropic Polypeptide
ADMET – Absorption, Distribution, Metabolism, Excretion, Toxicity
CNN – Convolutional Neural Network
RNN – Recurrent Neural Network
GNN – Graph Neural Network
VAE – Variational Autoencoder
GAN – Generative Adversarial Network
MM/PBSA – Molecular Mechanics/Poisson–Boltzmann Surface Area
PDB – Protein Data Bank
SDF – Structure Data File
TPSA – Topological Polar Surface Area

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the Ochsner Center for Outcomes Research, Ochsner Research, Ochsner Clinic Foundation, New Orleans, LA 70121, USA, for providing the necessary facilities to carry out this study. The authors also extend their sincere thanks to the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA, for their support and provision of essential resources to complete this work.

FUNDING

This research received no external funding. All work was conducted using institutional resources without dedicated grant support.

CONFLICT OF INTEREST

The authors declare that they have no known financial, personal, academic, or other relationships that could inappropriately influence, or be perceived to influence, the work reported in this manuscript. All authors confirm that there are no competing interests to declare.

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