Drug Repurposing: Finding New Uses for Approved Drugs¶
Industry: Pharmaceutical R&D | Role: VP Translational Science, Computational Biology Lead | Time to value: 2-3 hours
The Problem¶
There are thousands of approved drugs with well-characterized safety profiles. There are thousands of diseases with unmet therapeutic need. Somewhere in the intersection are repurposing candidates — approved drugs that could treat diseases they were never designed for. The challenge is finding them systematically, ranking them by evidence strength, and explaining why each candidate was predicted.
The Traditional Approach¶
Computational repurposing pipelines are typically assembled from separate components: literature mining to extract drug-target and target-disease associations, custom graph traversal code to walk protein-protein interaction networks, bespoke aggregation logic to combine evidence from multiple mechanistic pathways, a separate safety scoring module pulling adverse event data, and a structural similarity search for analogue discovery. A team of 3-4 computational biologists spends a sprint wiring these together — typically 500+ lines of Python — with no built-in explainability. When a reviewer asks "why was Drug X predicted for Disease Y?", someone traces the logic manually. Adding a new evidence source means modifying the traversal code.
With Uni¶
The notebook encodes the entire pipeline in 15 declarative rules. Multi-hop protein-protein interaction traversal finds indirect mechanistic paths from drug targets to disease-associated proteins. Noisy-OR aggregation combines evidence from independent pathways — if a drug reaches a disease through three separate mechanisms, each adds independent support. Safety penalties down-weight drugs with severe adverse reactions. Structural analogue discovery identifies approved drugs with similar binding profiles. Novelty filtering removes known indications. Built-in EXPLAIN traces exactly why each drug-disease pair was predicted — the full mechanistic derivation tree. ASSUME simulates hypothetical new binding targets. ABDUCE suggests what minimal evidence would support a new indication.
What You'll See¶
- 28 novel drug-disease candidates ranked by combined pathway evidence
- Safety-adjusted scores that penalize drugs with severe side effects
- Structural analogue matches linking candidates to known approved drugs
- Full mechanistic derivation trees for every prediction — reviewable by domain experts
- What-if simulation of new protein targets showing how hypothetical evidence changes rankings
Why It Matters¶
The same analysis that traditionally requires a team, a sprint, and 500+ lines of custom code is expressed in 15 rules that a domain expert can read, audit, and extend. That changes repurposing from a software engineering project to a scientific reasoning exercise.