Abstract Drug discovery has evolved from empirical use of natural resources to molecular target-based approaches driven by genomics and computational biology. The traditional “one drug–one target–one disease” model, while once effective, has proven inadequate for complex, multifactorial diseases such as Alzheimer’s and cancer. Network pharmacology has emerged as a transformative framework that integrates systems biology, omics data, and computational modeling to understand drug–target–disease interactions at a systems level. By representing biological entities (genes, proteins, metabolites, and drugs) as nodes and their relationships as edges, network pharmacology provides a holistic map of pharmacological space. This approach facilitates the identification of key hubs, functional modules, and multi-target drug candidates, advancing personalized and precision medicine. It also supports drug repurposing, predicts drug–drug interactions, and provides mechanistic insights into traditional medicines through network-based analysis of complex formulations. Tools such as Cytoscape, AutoDock, and databases like DrugBank, Kyoto Encyclopedia of Genes and Genomes, and Traditional Chinese Medicine Systems Pharmacology enable visualization, docking, and pathway mapping to decode molecular interactions. Despite its immense potential, challenges remain, including data quality, incomplete biological networks, and limited experimental validation. Nonetheless, network pharmacology represents a paradigm shift toward rational, multi-target therapeutics that better reflect biological complexity and clinical reality.
Tejesh et al. (2025) studied this question.