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Datasets:
PatSnap
/
chemical-structure

Tasks:
Feature Extraction
Modalities:
Text
Formats:
json
Languages:
English
Size:
1M - 10M
Tags:
chemistry
drug-discovery
cheminformatics
smiles
molecular-generation
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
license: cc-by-4.0
task_categories:
  - feature-extraction
language:
  - en
tags:
  - chemistry
  - drug-discovery
  - cheminformatics
  - smiles
  - molecular-generation
pretty_name: Curated Chemical Structures & Properties
size_categories:
  - 1M<n<10M

⚗️ Dataset Summary

A large-scale chemical structure database with 1 million compounds, providing standardized cheminformatics identifiers and taxonomic classification. Designed for structure-based drug discovery, similarity search, and chemical space analysis.

🚀 Key Features

  • Standard Identifiers: Every record includes InChI, InChIKey, and molecular formula; isomeric SMILES present for 99.8% of records.
  • Multiple Name Forms: JChem-generated names (97%), IUPAC names (22%), and compound names where available.
  • ClassyFire Taxonomy: Hierarchical chemical classification for ~11% of records, enabling class-level filtering.
  • Patent Linkage: patent_count connects compounds to their IP landscape.

💻 Quick Start

from datasets import load_dataset

dataset = load_dataset("your-org/chemical-structures", split="train")
print(dataset[0]["inchi_key"])
# Output: 'AAACIFUGYABHIU-UHFFFAOYSA-M'
print(dataset[0]["isometric_smiles"])
# Output: '[O-][Cl](=O)(=O)=O.[I-][Pt++]12[NH2]CC[NH]1CC[NH2]2'
print(dataset[0]["mol_formula"])
# Output: 'C4H13ClIN3O4Pt'

🔗 Resources