Can I use ToolUniverse for automated drug discovery?

Yes, it allows you to compose multi-step workflows that chain target discovery, protein structure prediction, and virtual screening into a single automated pipeline.

Which databases are accessible through this skill?

It provides standardized access to major scientific databases like OpenTargets, PubChem, UniProt, PDB, ChEMBL, and KEGG.

Do I need a GPU to use the tool discovery feature?

No, while the embedding-based search can use a GPU, the skill provides an LLM-based search and keyword search that work without specialized hardware.

What scientific domains does ToolUniverse support?

It supports a wide range of disciplines including bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery.

Is this skill compatible with Claude Desktop?

Yes, ToolUniverse integrates via the Model Context Protocol (MCP), making it available for use in both Claude Desktop and Claude Code.

ToolUniverse

Name: ToolUniverse
Author: Activer007

byActiver007

데이터 과학 및 ML

Orchestrates over 600 scientific tools and datasets for bioinformatics, drug discovery, and advanced computational research workflows.

소개

ToolUniverse transforms Claude into a specialized research scientist by providing a unified ecosystem to discover, execute, and compose scientific workflows across domains like genomics, proteomics, and cheminformatics. It offers standardized access to 600+ curated resources, including major databases like PubChem, UniProt, and OpenTargets, enabling the creation of complex multi-step pipelines for tasks such as protein structure prediction, virtual screening, and clinical data analysis via a seamless Model Context Protocol (MCP) integration.

주요 기능

Access to 600+ standardized scientific tools, ML models, and APIs
Integration with major databases like AlphaFold, ChEMBL, and PDB
0 GitHub stars
Automated composition of multi-step scientific research pipelines
Support for standardized AI-Tool Interaction Protocols (MCP)
Semantic and keyword-based discovery for specialized research tools

사용 사례

Protein structure prediction and binding site detection for structural biology
End-to-end drug discovery pipelines from target identification to virtual screening
Large-scale genomic analysis and pathway enrichment using public datasets

소개

주요 기능

Access to 600+ standardized scientific tools, ML models, and APIs
Integration with major databases like AlphaFold, ChEMBL, and PDB
0 GitHub stars
Automated composition of multi-step scientific research pipelines
Support for standardized AI-Tool Interaction Protocols (MCP)
Semantic and keyword-based discovery for specialized research tools

사용 사례

Protein structure prediction and binding site detection for structural biology
End-to-end drug discovery pipelines from target identification to virtual screening
Large-scale genomic analysis and pathway enrichment using public datasets