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Somewhere on a Pharmacy Shelf Might Be a Cure No One's Looked For Yet
Every drug works by interacting with specific targets in the body, and every disease involves specific disrupted pathways. This pathway trains you to map drugs, targets, and diseases as one connected network — and use that network to find new drug targets, or find new uses for drugs that already exist.
Two Questions, One Method
How do you find a brand-new drug target? And is there already an approved, safe drug that could treat a different disease than the one it was designed for? Both come down to network proximity — how close a drug's targets sit to a disease's disrupted pathway.
— Build networks connecting drugs to their known protein targets, and targets to the diseases they affect
— Calculate network proximity to generate repurposing candidates — the method behind real published discoveries
— Evaluate what makes a good new drug target: is it essential to the disease process, and realistically druggable?
— Use known side effects as a clue — drugs with similar side-effect profiles sometimes share a hidden mechanism
Why It Matters
Skipping a Decade and a Billion Dollars
Developing a brand-new drug typically takes over a decade and costs well over a billion dollars, and most candidates fail. Repurposing an already-approved drug skips most of that timeline and cost, because its safety profile is already established — you just need strong biological evidence it could work for a new disease. Network pharmacology is one of the main tools generating that evidence, and it has already led to real repurposed treatments now in clinical use.
What a Summer Project Looks Like
Network Pharmacology
Could an Existing Drug Treat a New Disease? A Network Proximity Repurposing Study
A student selects a disease of interest with a well-characterized set of disrupted genes or pathways, then builds a drug-target network using public pharmacology databases. Using network proximity methods, they screen a library of already-approved drugs to identify candidates whose target profile sits unusually close to the disease's network. They cross-check top candidates against known side-effect data and existing literature to see whether prior evidence supports or contradicts their computational finding. The project produces a ranked candidate list with supporting visualizations and a written rationale, mentored by a network pharmacology or computational drug discovery researcher.
Network proximity methods
Target validation reasoning
Literature cross-referencing
How Each Level Gets There
Every track takes a shared Fall foundations course, then this area-specific Spring course, then a live mentored summer project.
High School
HS-S-PM
Undergraduate
UG-S-PM
Medical Student
MS-S-PM
Clinician
CL-S-PM
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