Integrating Systems Biology and Artificial Intelligence in Traditional Pharmacy Research: Advancements, Challenges, and Opportunities

Abstract

In the age of artificial intelligence (AI) and biomedical big data, network pharmacology represents a breakthrough in traditional medicine (TM) research. The emergence of interdisciplinary frontiers, such as bioinformatics and systems medicine, has led to a new pharmaceutical research generation that emphasize networks and systems (1) (2). In recent years, TM researchers have shown great interest in exploring AI technologies as an emerging discipline (3). The network pharmacology field has proven to be an effective means of elucidating the mechanisms of traditional herbal medicine and traditional pharmacy (4). The primary focus is to modernize TM by incorporating cutting-edge techniques in genomics, metabolomics, and systems biology. This will enable a fresh look at the knowledge and insights offered by TM (5). Systems biology, which takes a holistic approach, is a crucial research methodology for understanding the TM pharmacology. To successfully integrate systems biology into TM, it is necessary to combine computational technologies with holistic insights (6). By constructing a network of interrelated "herb-compound-target-pathway" relationships, this technique provides a holistic understanding of the mechanisms underlying traditional medicine. The integration of computational techniques into the network pharmacology has led to a significant improvement in the accuracy and efficiency of active constituent screening and target identification, surpassing previous levels of performance (4). On the other hand, there has been a gradual increase in the global studies of traditional medicinal plants due to their natural sources and wide variety. These plants are capable of complementing modern pharmacological approaches (7-10).

Keywords

• network pharmacology
• traditional pharmacy
• persian medicine