Self-diagnosis and treatment by consumers as a means of reducing medical
costs contribute to the predicted continued growth in the usage of herbal products.
Herbal products are notoriously difficult to evaluate for potential drug interactions
because of the wide range of possible interactions, the lack of clarity surrounding the
active components, and the often insufficient knowledge of the pharmacokinetics of the
offending constituents. It is a standard practice for innovative drugs in development to
identify particular components from herbal goods and describe their interaction
potential as part of a systematic study of herbal product drug interaction risk. By
cutting down on expenses and development times, computer-assisted drug design has
helped speed up the drug discovery process. The natural origins and variety of
traditional medicinal herbs make them an attractive area of study as a complement to
modern pharmaceuticals. To better understand the pharmacological foundation of the
actions of traditional medicinal plants, researchers have increasingly turned to in silico
approaches, including virtual screening and network analysis. The combination of
virtual screening and network pharmacology can reduce costs and improve efficiency
in the identification of innovative drugs by increasing the proportion of active
compounds among candidates and by providing an appropriate demonstration of the
mechanism of action of medicinal plants. In this chapter, we propose a thorough
technical route that utilizes several in silico approaches to discover the pharmacological
foundation of the effects of medicinal plants. This involves discussing the software
used in the prediction of herb-drug interaction with a suitable database.
Keywords: Candidates, Computer-assisted, Cost, Composition, Components, Drug discovery, Drug design, Expenditures, Efficiency, Herbal products, In silico, Interactions, Medical, Medicinal plant, Pharmaceutical, Pharmacokinetics, Selfdiagnosis, Traditional, Treatment, Virtual screening.
