ChEMBL-36 / README.md
lukaskim's picture
Update README.md
d61c82c verified
metadata
license: cc-by-sa-4.0
pretty_name: ChEMBL 36
tags:
  - chemistry
  - drug-discovery
  - biology
  - smiles
  - proteins
  - bioactivity
size_categories:
  - 1M<n<10M
configs:
  - config_name: molecules
    data_files: molecules/train-*.parquet
  - config_name: targets
    data_files: targets/train-*.parquet
  - config_name: molecule_target_pairs
    data_files: molecule_target_pairs/train-*.parquet

ChEMBL 36

ChEMBL 36 converted to HuggingFace datasets format. Source data is from the ChEMBL database (EMBL-EBI), a manually curated database of bioactive molecules with drug-like properties.

Dataset configs

molecules (~2.4M rows)

All compounds in ChEMBL with canonical SMILES representations.

Column Type Description
chembl_id string ChEMBL compound identifier
canonical_smiles string Canonical SMILES representation
standard_inchi string Standard InChI representation
standard_inchi_key string Standard InChI key (27-char hash; use for cross-database deduplication)
molecule_type string Compound type (e.g. Small molecule)
max_phase float64 Highest clinical trial phase reached (0–4; 4 = approved drug; null = not tested)
first_approval float64 Year of first regulatory approval, if approved
oral int64 Orally bioavailable flag (1/0)
prodrug int64 Prodrug flag (1/0)
natural_product int64 Natural product flag (1/0)
black_box_warning int64 FDA black-box warning flag (1/0)
withdrawn_flag int64 Withdrawn from market flag (1/0)
therapeutic_flag int64 Has a documented therapeutic use (1/0)
mw_freebase float64 Molecular weight of the free base form
alogp float64 Calculated lipophilicity (Wildman–Crippen LogP)
hba float64 Number of hydrogen bond acceptors
hbd float64 Number of hydrogen bond donors
psa float64 Polar surface area (Ų)
rtb float64 Number of rotatable bonds
aromatic_rings float64 Number of aromatic rings
heavy_atoms float64 Number of heavy atoms
qed_weighted float64 Quantitative Estimate of Drug-likeness (0–1)
num_ro5_violations float64 Number of Lipinski Rule-of-Five violations (0–4)

targets (~15K rows)

Protein targets with amino-acid sequences. Multi-component targets (protein complexes) produce one row per component.

Column Type Description
target_chembl_id string ChEMBL target identifier
pref_name string Preferred target name
target_type string Target type (e.g. SINGLE PROTEIN, PROTEIN COMPLEX)
organism string Source organism (e.g. Homo sapiens)
tax_id int64 NCBI taxonomy ID
accession string UniProt accession
sequence string Amino-acid sequence
gene_names string Comma-separated HGNC gene symbol(s) (e.g. EGFR); null for non-gene targets
protein_class_l1 string Top-level protein family (e.g. Kinase, GPCR); null if unclassified
protein_class_l2 string Second-level protein family; null if unclassified

molecule_target_pairs (~1–5M rows)

Paired bioactivity records linking compounds to protein targets. Filtered to rows with non-null SMILES, sequence, and pChEMBL value. All included measurements use standard_relation = '=' and standard_units = 'nM'.

Column Type Description
chembl_id string ChEMBL compound identifier
canonical_smiles string Canonical SMILES representation
target_chembl_id string ChEMBL target identifier
target_pref_name string Preferred target name
organism string Target organism
sequence string Amino-acid sequence (|-separated for multi-component targets)
standard_type string Activity type (e.g. IC50, Ki, Kd)
standard_value float64 Raw activity value (nM)
pchembl_value float64 Standardized −log₁₀ activity value
assay_type string Assay classification (e.g. B for binding)
confidence_score int64 Target-assignment confidence 0–9 (9 = direct single-protein assay)

Usage

from datasets import load_dataset

molecules = load_dataset("your-org/chembl-36", "molecules")
targets   = load_dataset("your-org/chembl-36", "targets")
pairs     = load_dataset("your-org/chembl-36", "molecule_target_pairs")

Source

Built from the ChEMBL 36 SQLite release using chembl-hf.

License

ChEMBL data is released under CC BY-SA 4.0. If you use this dataset, please cite the ChEMBL publication:

Zdrazil B, et al. (2024). The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity complementary data resources. Nucleic Acids Research, 52(D1), D1180–D1192. https://doi.org/10.1093/nar/gkad1004