Literature mining, ontologies and information visualization for drug repurposing.

Area of Science:

• Biomedical Informatics
• Pharmacology
• Computational Biology

Background:

• MEDLINE's growth necessitates advanced methods for extracting biomedical knowledge from text.
• Ontologies are crucial for organizing biomedical concepts and relationships.
• Drug repurposing (DR) is gaining importance as an alternative to traditional drug development due to industry productivity challenges.

Purpose of the Study:

• To review the application of literature mining and ontology modeling in drug repurposing (DR).
• To highlight systematic approaches for DR, integrating various in silico and high-throughput screening technologies.
• To present methods for integrating literature mining with other data types and visualization tools for discovering novel drug-indication associations.

Main Methods:

• Literature mining techniques for extracting biomedical terms and relations.
• Ontology modeling and traversal for knowledge discovery.
• Integration of literature mining with in silico, genomic, and high-throughput screening data.
• Utilization of visualization tools for identifying novel drug repurposing candidates.

Main Results:

• Literature mining and ontologies provide a framework for hypothesis generation in drug discovery and repurposing.
• Systematic approaches combining literature mining with other data sources enhance DR efficiency.
• Integration strategies facilitate the discovery of new indications for existing drugs.

Conclusions:

• Literature mining and ontology-based approaches are valuable tools for accelerating drug repurposing.
• Integrating diverse data sources and visualization tools can uncover novel therapeutic opportunities.
• These methods offer a promising avenue to address the declining productivity in biopharmaceutical research and development.