Chemical Entities of Biological Interest (ChEBI)

Chemical Entities of Biological Interest (ChEBI) is a comprehensive database and ontology of molecular entities, with a particular focus on small chemical compounds [1] [2]. It provides a structured vocabulary for describing constitutionally or isotopically distinct atoms, molecules, ions, radicals, complexes, and related chemical entities, including both naturally occurring substances and synthetic compounds relevant to biological systems [1] [2]. ChEBI incorporates an ontological classification system that organizes entities into parent-child relationships and supports the representation of chemical roles, structural classes, and molecular relationships [2] [1]. Widely used in bioinformatics, cheminformatics, and systems biology, ChEBI enables consistent chemical annotation, interoperability between databases, and integration of chemical knowledge across diverse scientific resources [2] [1]. By providing a standardized semantic framework for chemical entities, ChEBI supports data sharing, advanced querying, and computational analysis across the life sciences [1] [2].

Example Usage: Annotate a metabolomics dataset with ChEBI terms to identify compounds such as glucose or ATP, specify their chemical classification and biological roles, and support standardized annotation, pathway analysis, and cross-database integration [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

2433610

Total Edges

6913389

Root Nodes

609907

Leaf Nodes

1528418
Knowledge Coverage Statistics

Classes

220816

Individuals

0

Properties

10
Hierarchical Metrics

Maximum Depth

6

Minimum Depth

0

Average Depth

1.14

Depth Variance

0.69
Breadth Metrics

Maximum Breadth

908127

Minimum Breadth

26

Average Breadth

310545.00

Breadth Variance

135103408992.57
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

739967

Non-taxonomic Relations

0

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import ChEBI

ontology = ChEBI()
ontology.load("path/to/ChEBI-ontology.owl")

# Extract datasets
data = ontology.extract()

# Access specific relations
term_types = data.term_typings
taxonomic_relations = data.type_taxonomies
non_taxonomic_relations = data.type_non_taxonomic_relations

References