Molecular Process Ontology (MOP)

The Molecular Process Ontology (MOP) is a systematic vocabulary for describing and classifying molecular-level chemical processes and transformations that occur in organic chemistry. It provides formal definitions of common molecular processes such as cyclization, methylation, demethylation, oxidation, reduction, and other fundamental reaction steps underlying named reactions in chemistry. MOP serves as the foundational semantic layer for the Reaction Ontology (RXNO), enabling precise description of chemical reaction mechanisms and their constituent molecular processes. The ontology facilitates integration of chemical databases, computational chemistry platforms, and reaction informatics systems by providing standardized semantic representations of molecular transformations. MOP enables advanced searching and classification of reactions based on their underlying molecular mechanisms.

Example Usage: Represent a cyclization reaction step in RXNO by linking to MOP terms for the specific cyclization type (e.g., “6-membered ring closure”), enabling automated discovery of similar reactions across chemical databases.

Metrics & Statistics

Graph Statistics

Total Nodes

15794

Total Edges

41157

Root Nodes

3693

Leaf Nodes

8182

Knowledge Coverage Statistics

Classes

3717

Individuals

0

Properties

11

Hierarchical Metrics

Maximum Depth

6

Minimum Depth

0

Average Depth

1.09

Depth Variance

0.63

Breadth Metrics

Maximum Breadth

7300

Minimum Breadth

3

Average Breadth

2253.14

Breadth Variance

7474153.55

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

3840

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 MOP

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