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Tooluniverse Drug Target Validation

Checked
mims-harvard·vlatest·other·Apache-2.0

Validates potential drug targets using diverse data sources and predictive models from the ToolUniverse platform.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mims-harvard/tooluniverse --skill tooluniverse-drug-target-validation -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mims-harvard

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Live repository

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

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    Scanned mims-harvard/tooluniverse at f5c37eebeb.

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Commit
f5c37eebeb
Manifest
skills/tooluniverse-drug-target-validation/SKILL.md
Scanned
16 files, 4,014 lines
Copies in repo
3 — all of them scanned
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About this skill

What it does

This skill, Tooluniverse Drug Target Validation, helps AI agents evaluate potential drug targets using a structured, data-driven approach. It assesses target hypotheses against four key criteria: genetic evidence, druggability, safety (essentiality in normal tissue), and competitive landscape. The tool produces a quantitative Target Validation Score (0-100) with a priority tier classification and a GO/NO-GO recommendation to inform decisions before committing to laboratory experiments.

When to use it

  • When evaluating potential drug targets for a specific disease.
  • Before initiating wet-lab validation of a drug target hypothesis.
  • To prioritize drug targets based on computational evidence and risk assessment.
  • To quickly assess the viability of a target by identifying early "no-go" indicators.

Key capabilities

  • Genetic Evidence Assessment: Evaluates links to disease using data from OpenTargets and GWAS Catalog.
  • Druggability Analysis: Checks structure availability, binding pocket prediction (ProteinsPlus), and considers target class.
  • Safety Evaluation: Assesses expression in critical tissues and potential lethality based on mouse knockout studies.
  • Competitive Landscape Review: Identifies existing or late-stage drugs targeting the same pathway using ChEMBL, DrugBank, and ClinicalTrials.gov.
  • Quantitative Scoring: Generates a Target Validation Score (0-100) to summarize overall viability.
  • Automated Data Retrieval & Analysis: Uses Python code via Bash to retrieve data from ToolUniverse tools and perform statistical analysis.

Example prompts

  • "Evaluate [Target Name] as a drug target for [Disease Name]."
  • "What is the Target Validation Score for [Target Name]?"
  • "Assess the druggability of [Target Name]."
  • “Can you tell me about any safety concerns associated with targeting [Target Name]?”

Tips & gotchas

  • Prioritize Disease Association: Do not proceed to later analysis phases without first establishing a clear disease association.
  • Data Verification is Crucial: Always verify data from databases (GTEx, HPA, ClinicalTrials.gov) rather than making assumptions.
  • Computational Analysis Required: The skill relies on automated Python code execution; it does not provide descriptive summaries of analysis steps.