NETWORK PHARMACOLOGY IN MODERN DRUG DISCOVERY: FROM SYSTEMS-LEVEL TARGET IDENTIFICATION TO AI-ENABLED TRANSLATIONAL THERAPEUTICS

ABSTRACTNetwork pharmacology (NP) has emerged as a systems-level approach for understanding the complexinteractions between therapeutic compounds, biological targets, pathways, and disease processes. Incontrast to conventional single-target drug discovery, NP integrates systems biology, bioinformatics,network science, and pharmacological information to investigate multi-target mechanisms and identifypotential therapeutic opportunities. This review presents the conceptual foundations, major databases,computational resources, and standard workflows used in network pharmacology. It further discussesthe integration of protein–protein interaction analysis, functional enrichment, molecular docking,molecular dynamics, ADMET prediction, and experimental validation to strengthen mechanisticinterpretation. Particular attention is given to the growing integration of multi-omics data, drugrepurposing, polypharmacology, and natural-product research. Recent developments involvingnetwork propagation, graph-based approaches, artificial intelligence, and knowledge graphs are alsoconsidered for improving target identification, drug-response prediction, and patient-specifictherapeutic strategies. Despite these advances, database bias, incomplete networks, uncertainty intarget prediction, reproducibility issues, and limited experimental and clinical validation remainimportant challenges. Overall, NP provides an evidence-integration framework that can prioritizetestable hypotheses, reveal complex drug–disease relationships, and support more efficient andmechanistically informed drug discovery.