Artificial intelligence-guided biosynthesis and retrosynthesis in pharmacognosy: Toward the synthesis-oriented discovery of natural-product therapeutics en en Artificial intelligence is reshaping Pharmacognosy by connecting ethnobotanical knowledge, multi-omic data, Biosynthetic pathway prediction, Retrosynthetic planning, and medicinal chemistry optimization. Particular attention is given to AI-driven tools, including BioNavi-NP, graph-sequence-enhanced transformers, NAG2G, RSGPT, RetroExplainer, and human-in-the-loop systems such as DeepRetro. These platforms can reconstruct natural-product biosynthesis, predict plausible precursors, preserve molecular topology, suggest multi-step disconnections, and explore broad reaction spaces. They are especially relevant for metabolites with dense stereochemistry, unusual ring systems, multifunctional scaffolds, and enzyme-guided biosynthetic logic. Beyond route design, AI may help prioritize biosynthetic genes, optimize scarce plant-derived compounds, and guide the development of more drug-like analogues with improved potency, selectivity, pharmacokinetic behavior, and synthetic accessibility. However, important limitations remain, including limited plant-specific reaction datasets, weak reaction-condition prediction, incomplete stereochemical and regioselective modeling, benchmark weaknesses, and the need for expert validation. Overall, AI is best understood as a decision-support layer linking biodiversity, traditional knowledge, biosynthetic logic, and experimental synthesis for responsible future therapeutic discovery and validation across modern natural-product-based drug discovery pipelines. 72 82 Kiarash Solouki Faculty of Pharmacy Cyprus International University, Nicosia 99258, Northern Cyprus, Mersin 10 Turkey Niloufar Moharrer Navaei Faculty of Pharmacy Cyprus International University, Nicosia 99258, Northern Cyprus, Mersin 10 Turkey Niloufar.navaei@yahoo.com Ayla Balkan Faculty of Pharmacy Bahçeşehir University, Nicosia 99258, Northern Cyprus, Mersin 10 Turkey Natural products Pharmacognosy Artificial intelligence Biosynthetic pathway prediction Retrosynthetic planning Vol 6 No 2 Paper 1.pdf [1] T. Adhikary, P. 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