AΙ-Driven Drug Repurposing: Applications and Challenges.

Area of Science:

• Computational biology and pharmacology
• Drug discovery and development

Background:

• Drug repurposing offers a faster, cheaper alternative to traditional drug development by utilizing existing drugs with known safety profiles.
• This strategy significantly reduces the time, cost, and failure rates inherent in discovering novel therapeutics.

Purpose of the Study:

• To review the pivotal role of Artificial Intelligence (AI) tools in advancing drug repurposing initiatives.
• To highlight AI applications in key medical areas, including oncology, neurodegenerative disorders, and rare diseases.

Main Methods:

• AI leverages computational techniques to process extensive biological and medical datasets.
• Predictive modeling is employed to identify biomolecular similarities and elucidate disease mechanisms.
• Analysis of AI's impact across diverse therapeutic domains.

Main Results:

• AI significantly enhances the efficiency of identifying potential drug candidates for repurposing.
• Demonstrated applications of AI in accelerating therapeutic discoveries for oncology, neurodegenerative, and rare diseases.
• Identified key challenges including data quality, interpretability, ethical considerations, and regulatory hurdles.

Conclusions:

• AI-driven drug repurposing represents a transformative approach in medical research and drug development.
• This innovative field holds the potential to address unmet medical needs more efficiently and cost-effectively.
• Continued research is necessary to overcome existing challenges and fully realize AI's capabilities in drug repurposing.