A1-Enabled Drug Discovery with Tulsi

Background: Ocimum tenuiflorum L. (syn. O. sanctum; Tulsi, holy basil) is a Lamiaceae herb central to Ayurveda and now a frequent subject of computational drug discovery. Objective: To review how artificial intelligence (AI) and related in silico methods have been applied to O. tenuiflorum over roughly the last five years, to separate what these studies show from what they do not, and to outline implications for society. Methods: A structured narrative review of recent literature (2021 to October 2026) was conducted using web-based searches, with older sources used only for background. Thirteen primary Tulsi-focused studies were charted by approach, target, disease area, and main finding. Findings: Most studies combine database mining (notably IMPPAT), ADMET filtering, network pharmacology, molecular docking, and molecular dynamics with targets spanning diabetes (DPP4, GAPDH, PPARδ), cancer (EGFR), inflammatory disease (AKT1, STAT3), and SARS-CoV-2 main protease. A smaller, emerging group applies machine learning or deep learning to metabolomics, hyperspectral imaging, and multi-omics. Experimental or clinical validation is sparse. Human trial evidence is limited in scale, and a 2023 regulatory-style risk assessment raised reproductive-safety precautions. Conclusion: Tulsi is a credible source of hypotheses, not yet of validated drug leads. The field's priority is closing the loop between prediction, standardized extracts, wet-lab assays, and well-designed trials, while protecting traditional knowledge and consumers.