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.
Network medicine leverages the topology of protein-protein interaction (PPI) networks to understand disease mechanisms and identify drug repurposing opportunities. We present NetworkMedicineEngine, a pure Python framework implementing core network medicine algorithms: disease module identification via largest connected component (LCC) analysis with permutation-based significance testing, module expansion via the DIAMOnD algorithm, drug-target network proximity computation, and disease-disease similarity analysis.
Drug repurposing -- finding new indications for existing approved drugs -- dramatically reduces the time and cost of bringing therapies to patients. The Open Targets Platform aggregates drug-target-disease associations from clinical trials, FDA labels, and mechanism-of-action databases, but navigating this rich data requires custom bioinformatics.
Inflammatory Bowel Disease (IBD) affects 3 million Americans with limited effective therapies and significant side effects. Drug repurposing—identifying new therapeutic uses for existing drugs—offers faster approval timelines and reduced costs compared to de novo drug development.