openmedical/registry
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lifesciences-research

imported

protocols/lifesciences-research

AI Agent wrappers for Life Sciences APIs (Open Targets, ChEMBL, UniProt). Accelerating drug discovery with Model Context Protocol (MCP) and FastMCP.

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Protocols & Guidelines
Subcategory
unknown
License
MIT(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
ai-agents · bioinformatics · chembl · claude-code · clinical-trials · computational-biology · drug-discovery · fastmcp
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

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    Open, MIT-licensed food & nutrition science research skills for Claude Code, Codex, and MiniMax Agent — multi-agent literature/systematic review, journal-aware writing, peer review, and figures,…

  • openevidence-mcpai-agents · claude-code

    First open-source OpenEvidence MCP server: browser-session medical research tools for Codex, Claude Code, and MCP clients

  • oncoteamclinical-trials · fastmcp

    AI patient advocacy tool for cancer treatment. Understand labs, find clinical trials, track treatment — all from your phone. Open source, used in active treatment.

  • DrugClawai-agents · bioinformatics · drug-discovery

    💊 AI Research Assistant for Accelerated Drug Discovery. 🦞

sources
  1. api.github.com/repos/donbr/lifesciences-research
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-02-26, 7 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/22.json→ .entries["lifesciences-research"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.