Leveraging machine learning for drug repurposing in rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) presents a significant challenge in clinical management because of the dearth of effective drugs despite advances in understanding its mechanisms. Drug repurposing has emerged as a promising strategy to address this gap, offering potential cost savings and expediting drug discovery. Notably, computational methods, particularly machine learning (ML), have shown promise in RA drug repurposing. In this review, we survey various drug-repurposing approaches, both classical and contemporary, highlighting the pivotal role of ML. We summarize RA candidate drugs identified through computational strategies and discuss prevailing challenges in this domain. Leveraging ML, alongside a deepening understanding of RA mechanisms, holds promise for enhancing pharmacological treatment options for patients with RA.