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PatSnap/chemical-structure

⚗️ 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… See the full description on the dataset page: https://huggingface.co/datasets/PatSnap/chemical-structure.

sourceHugging Facecc-by-4.0updated 5mo agoView on Hugging Face
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⚗️ 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

python
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