Application of artificial intelligence and machine learning in drug repurposing

Area of Science:

• Pharmacology
• Biomedical Informatics
• Computational Biology

Background:

• Drug repurposing accelerates drug discovery by identifying new uses for existing medications.
• It is a cost-effective strategy that supports precision medicine and addresses rare diseases.
• Limited disease biology information often hinders therapeutic candidate identification.

Purpose of the Study:

• To review challenges and successes in drug repurposing.
• To survey computational frameworks for drug repurposing based on data types and algorithms.
• To explore future directions driven by artificial intelligence.

Main Methods:

• Review of existing literature on drug repurposing approaches.
• Analysis of computational frameworks utilizing heterogeneous network mining and natural language processing.
• Integration and analysis of large-scale biomedical data using machine learning and AI.

Main Results:

• Drug repurposing offers a faster, more cost-effective drug discovery pathway.
• Machine learning and AI enhance data-driven repurposing pipelines.
• Technological advances provide new analytical strategies for identifying therapeutic candidates.

Conclusions:

• Drug repurposing is a vital tool for precision medicine and rare diseases.
• AI and machine learning are transforming computational drug repurposing.
• Generative AI is poised to drive future innovations in drug repurposing research.