drug-repurposing

Identify drug repurposing candidates across disease targets, drugs, and safety data.

1.6k|244|Updated Mar 3, 2025

Search Tags:#ChEMBL #OpenTargets #DrugBank #drug-repositioning #safety-analysis #literature-mining #drug-repurposing

Ready to run

npx skills add https://github.com/mims-harvard/ToolUniverse --skill drug-repurposing

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: drug-repurposing
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/skills/drug-repurposing
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill drug-repurposing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps researchers identify drug repurposing candidates by integrating disease-target associations, drug-target interactions, safety profiles, and literature evidence across multiple knowledge bases to rank and prioritize new indications.

Core Features & Use Cases

Multi-database integration: OpenTargets, DrugBank, DGIdb, ChEMBL, FAERS, PubMed, and ClinicalTrials.
Systematic scoring and risk filtering to prioritize candidates for experimental validation and rapid decision-making.
Quick-start workflows supporting target-based, compound-based, and disease-driven strategies with evidence mining and reporting.

Quick Start

Use this skill to scan for repurposing candidates for a disease or a drug, review ranked results, and inspect safety signals. Example: initialize ToolUniverse, fetch disease targets, pull candidate drugs, score, and export top results.

Frequently Asked Questions about drug-repurposing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify drug repurposing candidates using disease targets and safety data?▼

Drug repurposing identifies new indications by integrating disease-target associations, drug-target interactions, and safety profiles from multiple knowledge bases to rank and prioritize candidates. This approach enables rapid screening and evidence-based decision-making for experimental validation.

Can I use ChEMBL and OpenTargets data together for drug repositioning workflows?▼

Yes, drug repositioning integrates ChEMBL and OpenTargets alongside DrugBank, DGIdb, FAERS, PubMed, and ClinicalTrials data. Multi-database cross-validation ensures comprehensive evidence mining, structured scoring, and safety filtering for new indications.

What is the best way to score drug repurposing candidates across multiple databases?▼

The best way to score candidates is through a data-driven scoring system that applies structured ranking and risk filtering across integrated databases. This systematic process prioritizes compounds for experimental validation based on evidence strength and safety profiles.

Does drug repurposing support target-based and compound-based screening strategies?▼

Drug repurposing supports target-based, compound-based, and disease-driven workflows for rapid screening. These strategies fetch disease targets, pull candidate drugs, score results, and export top indications using cross-database validation and evidence mining.

How do I filter drug repurposing candidates by safety signals before experimental validation?▼

Safety filtering during drug repurposing leverages FAERS data and safety profiles to remove candidates with adverse risk signals. This systematic risk filtering ensures that only prioritized, safer candidates proceed to experimental validation and downstream decision-making.

When should I use a disease-driven workflow for drug repurposing instead of target-based screening?▼

Use a disease-driven workflow for drug repurposing when starting from a specific condition to fetch associated targets and pull candidate drugs. Target-based screening is preferable when your research begins with a known molecular target rather than a disease phenotype.