Chemical Information Ontology (CHEMINF)

The Chemical Information Ontology (CHEMINF) provides a comprehensive vocabulary for representing chemical information entities, including chemical descriptors, properties, algorithms, and computational methods used in cheminformatics [1] [2]. It supports standardized representation of molecular attributes and calculated or reported chemical information, enabling unambiguous description of qualitative and quantitative descriptors derived from chemical informatics tools and workflows [1] [2]. CHEMINF is designed to improve semantic interoperability across chemistry databases, computational chemistry platforms, and drug discovery systems by providing explicit definitions for chemical information concepts and their provenance [1] [2]. It can be used alongside related chemistry ontologies to link chemical structures with their calculated properties, descriptors, and generating methods [1] [2].

Example Usage: Represent a computed molecular descriptor as a CHEMINF information entity linked to a chemical structure, specifying the descriptor type, the calculation method, and the resulting numeric value, so that descriptor data can be queried, compared, and integrated across cheminformatics datasets and software environments [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

1467

Total Edges

2837

Root Nodes

213

Leaf Nodes

435
Knowledge Coverage Statistics

Classes

358

Individuals

0

Properties

52
Hierarchical Metrics

Maximum Depth

16

Minimum Depth

0

Average Depth

1.73

Depth Variance

9.21
Breadth Metrics

Maximum Breadth

213

Minimum Breadth

1

Average Breadth

29.24

Breadth Variance

3411.59
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

93

Non-taxonomic Relations

1

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import CHEMINF

ontology = CHEMINF()
ontology.load("path/to/CHEMINF-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