Artificial Intelligence in Drug Discovery for Rare Diseases

Abstract

Rare diseases affect a significant portion of the global population despite their individual rarity, yet therapeutic development remains limited due to economic and scientific constraints. Artificial intelligence (AI) has emerged as a transformative approach in pharmaceutical research, enabling the analysis of large-scale biological datasets and accelerating the identification of potential drug candidates. This study explores the role of machine learning and deep learning techniques in rare disease drug discovery. AI-driven models facilitate drug-target interaction prediction, molecular optimization, and drug repurposing, significantly reducing time and cost. The paper also discusses methodological frameworks, applications, challenges, and future directions of AI integration in pharmaceutical research. The findings indicate that AI has the potential to revolutionize rare disease treatment by improving efficiency, accuracy, and accessibility.