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<p>The Himalayan region represents a globally significant reservoir of medicinal plant diversity and indigenous ethnobotanical knowledge. However, despite its phytochemical richness and longstanding use in traditional healing systems, systematic scientific validation and integration into modern drug discovery remain limited. This review critically examines the therapeutic potential of Himalayan medicinal plants by bridging traditional ethnobotany with contemporary computational and biotechnological approaches. We describe evidence on regional biodiversity across longitudinal and latitudinal divisions, highlight major bioactive phytochemicals and their pharmacological relevance, and discuss conventional extraction and characterization techniques. Importantly, the review explores the transformative role of artificial intelligence (AI) in medicinal plant research, including taxonomic classification, multi-omics data integration, predictive modeling of secondary metabolite biosynthesis, QSAR analysis, molecular docking, virtual screening, polypharmacology assessment, and ADMET prediction. By integrating AI-driven chemo-informatics with systems-level multi-omics frameworks, this work outlines a scalable strategy for accelerating drug discovery from Himalayan flora while supporting conservation and ethical preservation of indigenous knowledge. The review identifies key research gaps and proposes future directions for sustainable, data-driven phytopharmaceutical development.</p>

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Synergizing ethnobotany and artificial intelligence: Exploring therapeutic frontier of Himalayan medicinal plants

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

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  • Divjot Kour,
  • Aman Kataria,
  • Abhishek Kumar,
  • Akshara Bassi,
  • Tanvir Kaur,
  • Meenakshi Gusain,
  • Imran Sheikh,
  • Sofia Sharief Khan,
  • Abhishek Dutta,
  • Simranjeet Kaur,
  • Harpreet Kour,
  • Parveen Bajaj,
  • George Uwadiegwu Alaneme

摘要

The Himalayan region represents a globally significant reservoir of medicinal plant diversity and indigenous ethnobotanical knowledge. However, despite its phytochemical richness and longstanding use in traditional healing systems, systematic scientific validation and integration into modern drug discovery remain limited. This review critically examines the therapeutic potential of Himalayan medicinal plants by bridging traditional ethnobotany with contemporary computational and biotechnological approaches. We describe evidence on regional biodiversity across longitudinal and latitudinal divisions, highlight major bioactive phytochemicals and their pharmacological relevance, and discuss conventional extraction and characterization techniques. Importantly, the review explores the transformative role of artificial intelligence (AI) in medicinal plant research, including taxonomic classification, multi-omics data integration, predictive modeling of secondary metabolite biosynthesis, QSAR analysis, molecular docking, virtual screening, polypharmacology assessment, and ADMET prediction. By integrating AI-driven chemo-informatics with systems-level multi-omics frameworks, this work outlines a scalable strategy for accelerating drug discovery from Himalayan flora while supporting conservation and ethical preservation of indigenous knowledge. The review identifies key research gaps and proposes future directions for sustainable, data-driven phytopharmaceutical development.