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Drug Repurposing with ToolUniverse · mims-harvard | DecimalAI

scienceby mims-harvard

Drug Repurposing with ToolUniverse

mims-harvard/tooluniverse-drug-repurposing

Source mims-harvard/ToolUniverse ↗License Apache-2.0

Identify drug repurposing candidates via target-based, compound-based, and disease-based strategies. Combines drug-target-disease network reasoning with mechanism rationale, clinical-trial precedent, and patent/regulatory feasibility.

SkillScore73Maintained

On-demandOn-demand: invoked explicitly — never occupies your agent's context until called.

Clinical / Healthcare pack

Last verified 8/6/2026

What it changes

Passed 12 of 17 test cases with the skill, and 6 of 17 without it.

Without skill36%

With skill73%

Checks it passed that the model alone missed

Assigns the evidence grade E1.
Assigns the evidence grade E4.
Uses query as the parameter name for target lookups.

and 5 more in the full report

8 fixed8 passed either way6 still failing0 broke

Pass-rate lift: +36 pp
Tokens: +159%
Agent turns: 0%
Response time: -42%

Model gemini-3.6-flashMeasured 8/6/2026v116 of 22 cases passed with skill · 17 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/mims-harvard-tooluniverse-drug-repurposing/SKILL.md https://app.decimal.ai/s/mims-harvard-tooluniverse-drug-repurposing/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add mims-harvard-tooluniverse-drug-repurposing, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/mims-harvard-tooluniverse-drug-repurposing/ and pins it in .decimal/skills.lock

decimalai skills add mims-harvard-tooluniverse-drug-repurposing

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/mims-harvard-tooluniverse-drug-repurposing/SKILL.md https://app.decimal.ai/s/mims-harvard-tooluniverse-drug-repurposing/SKILL.md

Raw file: /s/mims-harvard-tooluniverse-drug-repurposing/SKILL.md ↗ · machine-readable summary at /s/mims-harvard-tooluniverse-drug-repurposing.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 8

Prompt

To query FAERS report counts by drug name in ToolUniverse, which parameter key accepts the drug name argument?

What was checked, without and with the skill
Checked	Without skill	With skill
Uses medicinalproduct as the parameter key for the drug name argument.	Missed	Passed

case-11 · gemini-3.6-flash · 8/6/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md240 linessingle file

Drug Repurposing with ToolUniverse

Systematically identify and evaluate drug repurposing candidates using multiple computational strategies.

IMPORTANT: Always use English terms in tool calls. Respond in the user's language.

Reasoning Before Searching

Start by asking: WHY might this drug work for a new disease? Three strategies:

(a) Same target: The drug's primary target is also involved in the new disease. This is the strongest hypothesis — use OpenTargets to check if the target has genetic evidence in both diseases before any other search.
(b) Off-target activity: The drug has secondary targets or off-target effects that are relevant to the new disease. Check ChEMBL bioactivity data for all known targets of the drug, not just its primary one.
(c) Shared pathways: The original indication and new disease share molecular pathways, even if the target itself is not genetically linked. Use Reactome and STRING to compare pathway overlap between diseases.

Each strategy uses different tools and has different evidentiary weight. Identify which strategy applies FIRST, then choose the corresponding workflow below. Do not run all three strategies blindly — reason about which is most plausible given the drug's mechanism.

LOOK UP DON'T GUESS: Never assume a drug hits a target, never assume a target is disease-relevant, never assume pathway overlap. Verify each link with tool calls.

Core Strategies

Target-Based: Disease targets -> Find drugs that modulate those targets
Compound-Based: Approved drugs -> Find new disease indications
Disease-Driven: Disease -> Targets -> Match to existing drugs

Workflow Overview

Phase 1: Disease & Target Analysis
  Get disease info (OpenTargets), find associated targets, get target details

Phase 2: Drug Discovery
  Search DrugBank, DGIdb, ChEMBL for drugs targeting disease-associated genes
  Get drug details, indications, pharmacology

Phase 3: Safety & Feasibility Assessment
  FDA warnings, FAERS adverse events, drug interactions, ADMET predictions

Track record

Benchmark

12 / 17

test cases passed, vs 6 / 17 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

mims-harvard / ToolUniverse

skills/tooluniverse-drug-repurposing/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/2/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Drug Repurposing with ToolUniverse

Reasoning Before Searching

Core Strategies

Workflow Overview

Track record

Benchmark

Adoption

SkillSafety

Source

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