One promising way to optimize and improve the development of herbal
remedies is to incorporate artificial intelligence (AI) methodology into the field of
herbal medicinal formulations. AI methods are being used increasingly to analyze large
datasets that include traditional knowledge, pharmacological properties, botanical
compounds, and therapeutic effects. These methods include machine learning
algorithms, neural networks, and natural language processing. These computational
tools make it easier to identify bioactive ingredients, anticipate synergistic interactions,
and understand the molecular processes that underlie herbal formulations. Furthermore,
the process of drug discovery and development can be streamlined by using AI-driven
modeling to quickly screen formulations for safety, bioavailability, and efficacy. The
combination of AI and herbal medicine works well together to speed up the search for
new therapeutic combinations and facilitate comprehension of the complex interactions
between phytochemicals and their biological targets. However, issues like algorithm
robustness, ethical considerations, and data quality make more research and validation
in this emerging field necessary. However, the combination of AI techniques and
herbal medicine formulations has great potential to advance evidence-based and
personalized healthcare practices.
Keywords: Artificial intelligence, Computational modeling, Herbal medicine, Interactions, Medicinal formulations, Machine learning, Molecular mechanisms.
