python-chebi-utils

Common processing functionality for the ChEBI ontology — download versioned data files, build an ontology graph, extract molecules, assemble labeled datasets, generate stratified train/validation/test splits, extract first-order-logic molecular properties, and select hierarchy-aware sample subsets.

⚠️ Breaking change in v0.3

create_multilabel_splits now returns the validation split under the key "validation" instead of "val". Update any code that reads splits["val"] to use splits["validation"].

Installation

pip install chebi-utils

For development (includes pytest and ruff):

pip install -e ".[dev]"

Features

Download ChEBI data files

from chebi_utils import download_chebi_obo, download_chebi_sdf

obo_path = download_chebi_obo(version=248, dest_dir="data/")  # downloads chebi.obo
sdf_path = download_chebi_sdf(version=248, dest_dir="data/")  # downloads chebi.sdf.gz

A specific ChEBI release version (e.g. 230, 245, 248) must be provided. Files are fetched from the EBI FTP server. Versions below 245 are automatically fetched from the legacy archive path.

Build the ChEBI ontology graph

from chebi_utils import build_chebi_graph

graph = build_chebi_graph("chebi.obo")
# networkx.DiGraph — nodes are string ChEBI IDs (e.g. "1" for CHEBI:1)
# node attributes: name, smiles, subset
# edge attribute:  relation  ("is_a", "has_part", …)

Obsolete terms are excluded automatically. xref: lines are stripped before parsing to work around known fastobo compatibility issues in some ChEBI releases.

To obtain only the is_a hierarchy as a subgraph:

from chebi_utils.obo_extractor import get_hierarchy_subgraph

hierarchy = get_hierarchy_subgraph(graph)

Extract molecules

from chebi_utils import extract_molecules

molecules = extract_molecules("chebi.sdf.gz")
# DataFrame columns: chebi_id, name, inchi, inchikey, smiles, charge, mass, mol, …
# mol column contains RDKit Mol objects (None when parsing fails)

Both plain .sdf and gzip-compressed .sdf.gz files are supported. Molecules that cannot be parsed are excluded from the returned DataFrame.

Build a labeled dataset

from chebi_utils import build_labeled_dataset

dataset, labels = build_labeled_dataset(graph, molecules, min_molecules=50)
# dataset — DataFrame with columns: chebi_id, mol, <label1>, <label2>, …
#            one boolean column per selected ontology class
# labels  — sorted list of ChEBI IDs selected as label classes

Each molecule is assigned to every label class that it belongs to directly or through a chain of is_a relationships. Only classes with at least min_molecules descendant molecules are kept as labels.

Generate stratified train/val/test splits

from chebi_utils import create_multilabel_splits

splits = create_multilabel_splits(dataset, train_ratio=0.8, val_ratio=0.1, test_ratio=0.1)
train_df = splits["train"]
val_df = splits["validation"]  # renamed from "val" in v0.3
test_df = splits["test"]

Columns 0 and 1 (chebi_id, mol) are treated as metadata; all remaining columns are treated as binary label columns. When multiple label columns are present, MultilabelStratifiedShuffleSplit from the iterative-stratification package is used; for a single label column, StratifiedShuffleSplit from scikit-learn is used.

Extract molecular properties as first-order-logic facts

from chebi_utils.extract_properties import mol_to_fol_atoms, get_numerical_facts

atom_facts, mol_facts = mol_to_fol_atoms(mol, with_rings=True, with_steroids=True)
# atom_facts — dict[str, list] of predicates over atom indices:
#   unary  (e.g. "c", "charge_p", "has_2_hs", "cip_code_R", "in_ring6", "steroid_3")
#          → list[int] of atom indices
#   binary (e.g. "has_bond_to", "bSINGLE", "ring6") → list[tuple[int, ...]]
# mol_facts — set[str] of molecule-level predicates that hold for the whole
#             molecule (e.g. "net_charge_neutral", "aromatic")

numerical_facts = get_numerical_facts(mol)
# {"mol_weight": [<rounded MolWt>], "ring_size": [<size per ring>, …]}

Turns an RDKit Mol into a symbolic model suitable for building FOL structures for reasoning tasks. Facts cover per-atom element, formal charge, hydrogen counts, and CIP chirality; symmetric bond and bond-stereo relations; ring membership up to MAX_RING_SIZE (8); and steroid-nucleus positions (steroid_1 … steroid_17) matched against the gonane core via IUPAC steroid numbering. Ring and steroid extraction can be toggled with with_rings and with_steroids.

Select hierarchy-aware sample subsets

from chebi_utils.sample_filters import get_closest_negatives, get_direct_neighbors

# Nearest negatives: samples that are NOT subclasses of the target but close to
# it in the ontology, expanding outward until min_samples (up to max_samples) is met.
negatives = get_closest_negatives(
    samples, graph, target_id="15841", min_samples=25, max_samples=None
)

# Split samples into positives (descendants of the target) and "direct neighbor"
# negatives (descendants of ALL direct parents of the target, but not the target).
pos_ids, neg_ids = get_direct_neighbors(samples, graph, target_id="15841")

Useful for constructing balanced positive/negative sets for a given ChEBI class by leveraging the is_a hierarchy. samples is a list of ChEBI IDs (as strings) and graph is a graph from build_chebi_graph.

Running Tests

pytest tests/ -v

Linting

ruff check .
ruff format --check .

To run the same Ruff checks automatically before each commit:

pre-commit install

CI/CD

A GitHub Actions workflow (.github/workflows/ci.yml) automatically runs ruff linting and the full test suite on every push and pull request across Python 3.10, 3.11, and 3.12.