Network Pharmacology approaches can serve as a valuable tool for evidence-based Ayurveda to understand the medicines putative actions, indications, and mechanisms. A major portion of the network pharmacology is network analysis which mainly covers attribute analysis, topological analysis, network structure and stability, flow (flux) balance analysis and network models.
The one-drug/one-target/one-disease approach to drug discovery is presently facing many challenges of safety, efficacy and sustainability. Network biology and polypharmacology approaches gained appreciation recently as methods for omics data integration and multi target drug development, respectively. The combination of these two approaches created a advanced Paradigm called network pharmacology that looks at the effect of drugs on both the interaction and the disease level. A major portion of the network pharmacology is network analysis which mainly covers attribute analysis, topological analysis, network structure and stability, flow (flux) balance analysis and network models. A network analysis usually measures module, betweenness, hub, node, edge, shortest path and degree of hub gene.
This aid to develop combinatorial drug formulation to combat the pathogenic condition with lesser toxicity. There are various network structure (Protein-drug, protein-Pathway, protein-Molecular functions and gene-disease associations) were built and analyzed to scrutinize the effective drug combinations. The cumulative analysis of all these networks strongly suggests the appropriate bio-active compounds against desired targets. Enrichment of the protein networks leads to have insights on attributes such as molecular functions, pathways, disease association and cellular component localization.