NetworkPharmacologyEngine: Drug-Target Network Construction, Hub Target Identification, and Drug Repurposing Scoring
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Network pharmacology integrates drug-target interactions with biological networks to understand polypharmacology and identify repurposing opportunities. We present NetworkPharmacologyEngine, a pure-Python pipeline for network pharmacology analysis. The engine implements drug-target network construction, hub target identification (degree/betweenness centrality), drug repurposing scoring (network proximity), pathway enrichment of drug targets, and synergistic drug combination prediction. Applied to 100 drugs × 500 targets, the pipeline identifies mean 5.4 targets/drug, 39 hub targets, and top repurposing score=0.795.
Introduction
Network pharmacology models drugs as multi-target agents acting on biological networks. Hub targets are highly connected nodes that mediate drug effects. Network proximity measures how close drug targets are to disease genes in the interactome.
Methods
Network Construction
Drug-target edges from ChEMBL (IC50 < 1 µM). Target-target edges from STRING (score > 700).
Hub Targets
Hub = degree > mean + 2σ. Betweenness centrality by Brandes algorithm.
Repurposing Score
Proximity = mean shortest path from drug targets to disease genes.
Results
Mean targets/drug=5.4. Hub targets=39. Top repurposing=0.795.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: network-pharmacology-engine description: Drug-target network construction, hub target identification, and drug repurposing scoring allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/NetworkPharmacologyEngine cd NetworkPharmacologyEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python network_pharmacology_engine.py ``` 4. Output: `network_pharmacology_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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