Chemical reaction network knowledge graphs: the OntoRXN ontology

Area of Science:

• Computational Chemistry
• Data Management
• Semantic Web Technologies

Background:

• Managing large datasets is crucial across scientific disciplines.
• Semantic approaches, using ontologies, structure data and define entity relationships.
• Computational chemistry generates complex reaction network data requiring robust organization.

Purpose of the Study:

• Introduce OntoRXN, a novel ontology for reaction networks from computational chemistry.
• Provide a structured, graph-based representation for reaction data.
• Facilitate standardized analysis and workflow development for intricate chemical datasets.

Main Methods:

• Representing reaction networks as undirected graphs.
• Defining core classes: reaction steps, network stages, chemical species, and computational calculations.
• Leveraging the OntoCompChem ontology and the ioChem-BD database for data storage (CML format).
• Generating knowledge graphs from OntoRXN for various chemical systems.
• Utilizing SPARQL queries for knowledge graph exploration.

Main Results:

• OntoRXN successfully models reaction networks as undirected graphs.
• Knowledge graphs were generated for diverse chemical systems from ioChem-BD.
• SPARQL queries demonstrated effective data standardization and analysis.
• The ontology simplifies the development of complex computational chemistry workflows.

Conclusions:

• OntoRXN provides a powerful semantic framework for organizing and analyzing computational chemistry reaction data.
• The ontology enables standardized data interpretation and facilitates the creation of sophisticated analytical workflows.
• This approach enhances the discoverability and usability of complex chemical datasets.