
Tooluniverse Gwas Drug Discovery
This connects GWAS hits to actual drug targets by doing the full chain: variant to gene (via fine-mapping and eQTL), gene to druggable protein (via OpenTargets tractability), protein to existing compounds (via ChEMBL and DGIdb). The workflow is smart about the causal direction problem: a loss-of-function variant protecting against disease means you want an inhibitor, not an agonist. It includes a composite scoring system weighing genetic evidence, druggability, clinical data, and novelty. The documentation is unusually good about parameter gotchas (ensemblId vs ensemblID, disease_trait not trait). Best for target validation when you have genetic evidence and want to skip straight to what's druggable and whether something already exists for repurposing.
- skills.sh installs
- 388
- GitHub stars
- 1.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-drug-discovery --agent claude-codeInstalls into .claude/skills of the current project.
Files
GWAS-to-Drug Target Discovery
Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.
IMPORTANT: Always use English terms in tool calls. Respond in the user's language.
Overview
This skill bridges genetic discoveries from GWAS with drug development by:
- Identifying genetic risk factors - Finding genes associated with diseases
- Assessing druggability - Evaluating which genes can be targeted by drugs
- Prioritizing targets - Ranking candidates by genetic evidence strength
- Finding existing drugs - Discovering approved/investigational compounds
- Identifying repurposing opportunities - Matching drugs to new indications
Key insight: Targets with genetic support have 2x higher probability of clinical approval (Nelson et al., Nature Genetics 2015).
Reasoning Strategy
Sponsors
Details
- Repository
- github.com/mims-harvard/tooluniverse
- Categories
- Data Science & ML, Sales & Marketing
- First seen
- Jun 3, 2026
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