Network pharmacology in the multi-omics era: uncovering novel therapeutic strategies
Kalyani Ramprasad Thombre, Vaishnavi Gabhane, Krishna R. Gupta, Milind Janrao Umekar
Abstract
Network pharmacology signifies a paradigm shift from the conventional “one-drug, one-target” model toward a systems-oriented framework that captures the multifactorial nature of disease. By integrating computational modeling, bioinformatics, and multi-omics technologies—spanning genomics, proteomics, and metabolomics it enables the systematic mapping and mechanistic interrogation of complex biological networks. Advanced in silico approaches, including molecular docking, network propagation algorithms, and topology-based analyses, facilitate the identification of pivotal targets and pathways, while artificial intelligence and deep learning enhance predictive accuracy. Open-access platforms such as STRING, STITCH, TCMSP, and DisGeNET support data integration, functional annotation, and hypothesis generation. Integrating these insights with chemical informatics and structure–activity relationship (SAR) data establishes a bridge between systems biology and medicinal chemistry, guiding scaffold optimization and multitarget drug design. Network pharmacology has transformed drug repurposing, validated traditional therapeutics, and advanced precision medicine by enabling rational prediction of synergistic drug combinations and improved disease stratification. Despite ongoing challenges such as data heterogeneity and computational complexity emerging machine learning strategies and expanding datasets continue to refine predictive capability. Collectively, this integrative and predictive framework redefines the landscape of bioorganic and medicinal chemistry, facilitating the rational design of safer, more efficacious, and mechanism-guided therapeutics.