• Harnessing Artificial Intelligence for the Discovery and Development of Herbal Therapies in Treating Depression and Anxiety
  • Robab Bahreini,1 Neda Baghban,2 Aida Baghban,3,*
    1. Student Research and Technology Committee, Bushehr University of Medical Sciences, Bushehr, Iran
    2. Bushehr University of Medical Sciences, Bushehr, Iran
    3. Fasa University of Medical Sciences, Fasa, Iran


  • Introduction: Depression and anxiety are among the most widespread psychiatric disorders globally, necessitating innovative approaches to their treatment. Herbal medicines, with their multifaceted bioactive compounds and lower side effect profiles, have gained renewed interest. At the same time, Artificial Intelligence (AI) has emerged as a transformative tool in biomedical research. This review study aims to systematically explore the applications of AI in the discovery and development of herbal medicines targeted at treating depression and anxiety.
  • Methods: This review was conducted through a systematic search of peer-reviewed articles from major scientific databases including PubMed, Scopus, and Web of Science, focusing on literature published between 2010 and 2024. The inclusion criteria centered on studies that employed AI methodologies such as machine learning, neural networks, and natural language processing in analyzing phytochemical databases, predicting therapeutic potential, and streamlining drug development processes specific to mental health disorders. Data extraction focused on AI techniques, types of herbal compounds studied, and reported outcomes relevant to depression and anxiety.
  • Results: The review highlights a growing body of evidence supporting AI’s role in accelerating the identification of bioactive compounds, predicting synergistic effects, and reducing the time and cost associated with herbal drug development. AI-driven platforms were shown to enhance pattern recognition in phytochemical data, enable virtual screening for antidepressant and anxiolytic potential, and assist in formulating personalized herbal interventions. Several case studies demonstrated AI-facilitated repurposing of traditional herbal remedies with validated efficacy in preclinical and early clinical settings.
  • Conclusion: AI is increasingly recognized as a powerful enabler in the field of herbal psychopharmacology. Its integration into herbal medicine research promises to revolutionize how treatments for depression and anxiety are discovered, validated, and personalized. Further interdisciplinary studies are warranted to translate these findings into clinically actionable therapies and to develop robust AI frameworks tailored for traditional medicine systems.
  • Keywords: Artificial Intelligence, Herbal Medicine, Depression, Anxiety, Drug Development,