Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications

Area of Science:

• Bioinformatics
• Cheminformatics
• Natural Language Processing

Background:

• Drug development relies on understanding chemical properties from vast text data.
• Chemical text mining applications are crucial for extracting this information.
• Challenges remain, particularly in recognizing chemical entities within text.

Purpose of the Study:

• To review strategies for chemical entity recognition in text.
• To discuss other chemical text mining tasks like relationship extraction and structure determination.
• To highlight applications for drug side effects, toxicity, and network analysis.

Main Methods:

• Overview of basic concepts and principles for chemical entity recognition.
• Discussion of technical details with bibliographic references.
• Introduction to existing applications for building text mining pipelines.

Main Results:

• Chemical entity recognition is a key challenge in chemical text mining.
• Various methods and strategies exist for entity recognition and other related tasks.
• Published applications facilitate building pipelines for specific drug development analyses.

Conclusions:

• Chemical text mining is essential for drug development, despite current limitations.
• Advances in entity recognition and related tasks will improve information extraction.
• The field is expected to evolve, offering new possibilities for analyzing chemical data.