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Gdsc

Skill BioTender-max/awesome-bio-agent-skills/skills/drugclaw/gdsc

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.From the repository description

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill gdsc

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

2.9 KB, 867 tokens by cl100k_base, as published. Nobody here has run it

60_GDSC_GDSC2 — Genomics of Drug Sensitivity in Cancer

Overview

FieldValue
CategoryDrug-centric
SubcategoryDrug Molecular Property
SourceSanger / Wellcome Trust
Datasetsscreened_compounds (drug list), GDSC1/GDSC2 (dose-response), Cell Model Passports (cell-line annotations)
URLhttps://www.cancerrxgene.org/
Cell Modelshttps://cellmodelpassports.sanger.ac.uk/downloads

GDSC contains pharmacological profiles for ~500 drugs tested in ~1,000 cancer cell lines. Queryable entities include drug names, gene targets, pathways, and cell-line identifiers.

File Layout

DATA_DIR/
  ├── screened_compounds_rel_8.4.csv           # drug list (~100 KB)
  ├── GDSC1_fitted_dose_response_27Oct23.xlsx  # GDSC1 IC50/AUC (~80 MB, optional)
  └── GDSC2_fitted_dose_response_27Oct23.xlsx  # GDSC2 IC50/AUC (~50 MB, optional)

Default DATA_DIR:

resources_metadata/drug_molecular_property/GDSC

Override via environment variable: export GDSC_DATA_DIR=/your/path

Dependencies

conda install openpyxl   # or: pip install openpyxl

Download & Query

The script auto-downloads all data files (drug list CSV + GDSC1/GDSC2 dose-response XLSX) on first run if the data directory is empty.

CLI

# First run: auto-downloads all files, then queries default examples (Erlotinib, Nutlin, A549)
python 60_GDSC_GDSC2.py

If auto-download fails (e.g. no internet on HPC compute node), download manually from the repository root:

cd resources_metadata/drug_molecular_property/GDSC
wget 'https://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/screened_compounds_rel_8.4.csv'
wget 'https://cog.sanger.ac.uk/cancerrxgene/GDSC_data_8.5/GDSC1_fitted_dose_response_27Oct23.xlsx'
wget 'https://cog.sanger.ac.uk/cancerrxgene/GDSC_data_8.5/GDSC2_fitted_dose_response_27Oct23.xlsx'

Python API

from importlib.machinery import SourceFileLoader
mod = SourceFileLoader("gdsc", "60_GDSC_GDSC2.py").load_module()

# Single entity
results = mod.query_gdsc("Erlotinib")

# Multiple entities
results = mod.query_gdsc(["Nutlin", "A549", "EGFR"])

# Optional: manually trigger download
mod.download_gdsc_data()

Return Format

[
  {
    "source": "screened_compounds_rel_8.4.csv",
    "match_count": 1,
    "matches": [
      {
        "DRUG_NAME": "Erlotinib",
        "TARGET": "EGFR",
        "TARGET_PATHWAY": "EGFR signaling",
        "PUBCHEM_ID": "176870",
        "...": "..."
      }
    ]
  }
]
  • Returns an empty list when no matches are found.
  • Returns {"error": "..."} if the data directory is missing or empty.

LLM Integration Example

User:  "What is the target of Erlotinib in GDSC?"
Agent: calls query_gdsc("Erlotinib")
       → source: screened_compounds_rel_8.4.csv, TARGET: EGFR, PATHWAY: EGFR signaling
       → "Erlotinib targets EGFR (EGFR signaling pathway) according to GDSC."

What ships with it: 5 files

17.7 KB alongside SKILL.md, 4 of them executable

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