Abstract

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.