Basic Introduction

Network pharmacology is an interdisciplinary approach that integrates systems biology, bioinformatics, and pharmacology to understand drug actions from a network perspective. Unlike the traditional 'one drug, one target' paradigm, network pharmacology considers the multi-target and multi-pathway characteristics of drugs, especially for complex diseases and traditional medicine compounds. It helps to systematically explore the mechanisms of action and potential therapeutic effects.

Technical Principle

Network pharmacology explores drug actions by mapping interactions between compounds, targets, diseases, and biological pathways. Unlike the traditional “one drug, one target” approach, it embraces the multi-target nature of complex diseases and multi-component therapies.

The process typically includes identifying active compounds and predicting their targets using tools like SwissTargetPrediction. Disease-related genes are retrieved from databases such as GeneCards or DisGeNET. Overlapping targets are applied to construct a drug–target–disease network, often visualized in Cytoscape. Pathway enrichment analyses (e.g., GO, KEGG) assist in identifying key biological functions and mechanisms. Results may be validated via molecular docking or laboratory experiments.

This systems-level approach makes it possible to a deeper understanding of pharmacological mechanisms, facilitates drug repurposing, and is highly beneficial for studying traditional medicine and multi-target drug discovery.

Application Directions

- Mechanism exploration for traditional Chinese medicine and natural compounds

- Drug repurposing and discovery of multi-target agents

- Identifying synergistic drug combinations

- Prediction of potential therapeutic targets and pathways

- Systems-level understanding of disease mechanisms

Technical Advantages

- Captures the complexity of drug-disease interactions beyond single targets

- Supports integrative and holistic analysis, especially suitable for multi-component therapies

- Enables high-throughput prediction and visualization of drug mechanisms

- Reduces experimental costs by narrowing down target/pathway candidates

- Improves success rates in early drug development stages