Abstract

THEORETICAL FRAMEWORK OF NETWORK PHARMACOLOGY: METHODOLOGIES AND TOOLS

Dipali Jijabrao Mahajan, Damini Adhar Patil, Shweta Vilas Patil*, Dhanshree Ajay Mali, Vaibhav Vasant Patil and Prerana Sachin Bhavsar

ABSTRACT

Network pharmacology represents a paradigm shift from the traditional "one drug, one target" approach to a comprehensive "multi-target, multi-pathway" methodology for drug discovery and development. Integrating systems biology, computational modeling, and pharmacology enables the understanding of complex drug-disease interactions at a holistic level. This review explores the theoretical framework underpinning network pharmacology, focusing on methodologies such as data collection, network construction, analysis, and validation strategies. Key computational tools and databases that support network pharmacology research are highlighted, along with their applications in drug repurposing, multi-target drug development, and the study of traditional medicines. While network pharmacology offers significant advancements in drug discovery, challenges such as data standardization and computational complexity persist. This article provides insights into the methodologies and tools that drive network pharmacology, emphasizing its potential to transform modern medicine. Network pharmacology represents a paradigm shift from the traditional "one drug, one target" approach to a comprehensive "multi-target, multi-pathway" methodology for drug discovery and development. By integrating systems biology, bioinformatics, and pharmacology, it enables a deeper understanding of the complex interactions between drugs, targets, pathways, and diseases. This review explores the theoretical framework underpinning network pharmacology, focusing on methodologies such as data collection from genomic, proteomic, and metabolomic sources; network construction; and topological analysis for identifying critical nodes and pathways. The role of molecular docking and experimental validation in confirming computational predictions is also emphasized. Furthermore, key computational tools like Cytoscape, STRING, and AutoDock, as well as databases such as Drug Bank, KEGG, and OMIM, are discussed in detail. Applications of network pharmacology in drug repurposing, multi-target drug design, and the mechanistic study of traditional medicine are highlighted. While the field holds transformative potential, challenges such as data inconsistency, network complexity, and computational limitations remain barriers. Future integration of artificial intelligence and machine learning is proposed to address these challenges. This article aims to provide a comprehensive understanding of the methodologies and tools driving network pharmacology and their pivotal role in advancing modern drug discovery.

Keywords: The role of molecular docking and experimental validation in confirming computational predictions is also emphasized.