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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-code

Installs 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:

  1. Identifying genetic risk factors - Finding genes associated with diseases
  2. Assessing druggability - Evaluating which genes can be targeted by drugs
  3. Prioritizing targets - Ranking candidates by genetic evidence strength
  4. Finding existing drugs - Discovering approved/investigational compounds
  5. 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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