Core Predicted Targets

The term Core Predicted Targets refers to a collective grouping of proteins identified through computational methods rather than a single biological molecule or receptor. In the fields of network pharmacology and systems biology, these targets represent the most statistically significant or topologically central nodes within a protein-protein interaction network (Hopkins, 2008, Nature Chemical Biology). They are typically derived using algorithms that evaluate parameters such as degree centrality, betweenness, and closeness to determine which proteins are most likely to mediate the effects of a drug or a multi-component extract (Shao & Zhang, 2021, Journal of Ethnopharmacology). Because this is a descriptive label for a set of results, it does not possess a unique molecular classification, specific biological function, or a singular role in disease. Instead, the specific proteins within this group vary depending on the study, often including common hubs like AKT1, TP53, or TNF when investigating inflammatory or oncological conditions (Zhang et al., 2019, Evidence-Based Complementary and Alternative Medicine). As a result, Core Predicted Targets cannot be directly targeted by a drug; rather, the individual proteins identified within the list are the actual therapeutic targets. This terminology is used by biotech analysts to distinguish between experimentally validated targets and those suggested by predictive pipelines that require further laboratory confirmation.

Other names

Predicted core targetsKey predicted targetsCandidate core targetsNetwork hub targets

Not applicable