Leveraging machine learning for drug repurposing in rheumatoid arthritis

Area of Science:

• Rheumatology and Computational Pharmacology
• Drug Discovery and Development

Background:

• Rheumatoid arthritis (RA) management is challenging due to limited effective drug options.
• Understanding RA mechanisms has advanced, but clinical treatment gaps persist.
• Drug repurposing is a viable strategy to accelerate the discovery of new RA therapies.

Purpose of the Study:

• To review classical and contemporary drug repurposing approaches for rheumatoid arthritis.
• To highlight the significant role of machine learning (ML) in computational drug repurposing for RA.
• To summarize candidate drugs for RA identified via computational strategies.

Main Methods:

• Survey of existing literature on drug repurposing methodologies.
• Focus on computational strategies, particularly machine learning algorithms.
• Analysis of identified RA candidate drugs and associated challenges.

Main Results:

• Machine learning-based computational methods are effective in identifying potential RA drug candidates.
• Various drug repurposing strategies, both traditional and advanced, have been explored.
• Several candidate drugs have been identified through these computational approaches.

Conclusions:

• Machine learning and a deeper understanding of RA pathogenesis can significantly improve pharmacological treatments.
• Computational drug repurposing holds substantial promise for developing novel therapeutic options for RA patients.
• Addressing current challenges in computational RA drug discovery is crucial for future advancements.