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Datasets:
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target

Tasks:
Feature Extraction
Modalities:
Text
Formats:
json
Size:
1K - 10K
Tags:
genomics
drug-targets
bioinformatics
ensembl
ncbi
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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target / README.md
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metadata 
license: cc-by-4.0
pretty_name: Therapeutic Targets & Genomic Loci
tags:
  - genomics
  - drug-targets
  - bioinformatics
  - ensembl
  - ncbi
task_categories:
  - feature-extraction
size_categories:
  - 1K<n<10K

🎯 Dataset Summary

A drug target reference database covering 2,000 biological targets with cross-database identifier mapping and drug association counts. Designed for target-centric drug discovery and multi-omics integration.

🚀 Key Features

  • Cross-Database IDs: Each target links UniProt, NCBI Gene ID, Ensembl Gene ID, and RefSeq accessions (nucleotide and protein).
  • Multilingual Names & Aliases: target_name and alias support both English gene symbols and Chinese nomenclature.
  • Drug Count: drug_count provides a quick measure of target druggability based on associated compounds.

💻 Quick Start & MCP Integration

from datasets import load_dataset

dataset = load_dataset("your-org/drug-targets", split="train")
record = dataset[0]
en_name = next(n["name"] for n in record["target_name"] if n["lang"] == "EN")
print(en_name)
# Output: 'solute carrier family 37 member 4'
print(record["uniprot_id"])
# Output: ['O43826']
print(record["drug_count"])
# Output: 1

🔗 Resources