Multiple predicted targets based on network pharmacology of analogous TCM formulas

The term Multiple predicted targets based on network pharmacology of analogous TCM formulas does not refer to a single biological molecule, receptor, or enzyme. Instead, it describes a collection of potential protein or gene targets identified through network pharmacology, a systems-biology approach used to investigate the complex, multi-component interactions of Traditional Chinese Medicine (TCM) (Hopkins, 2008). This methodology integrates database mining, molecular docking, and network analysis to predict how various compounds within herbal formulas interact with human biological pathways (Zhang et al., 2019). Analogous TCM formulas—those with similar therapeutic effects—are often studied together to identify shared nodes, such as AKT1, TNF, or PTGS2, which may explain their common clinical efficacy (Li & Zhang, 2013). Because this entry represents a methodological result set rather than a discrete therapeutic target, it cannot be assigned a specific molecular classification or biological function. It serves as a conceptual grouping for researchers to understand the polypharmacological nature of herbal medicine rather than a validated drug target for drug development.