Artificial intelligence in the development of small nucleic acid therapeutics: toward smarter and safer medicines

Small nucleic acid therapeutics are revolutionizing disease treatment, with artificial intelligence assisting their design and chemical modifications, optimizing their therapeutic potential. • Overview of main classes of small nucleic acids, their mechanisms, and key chemical modifications. • Analysis of AI applications in oligonucleotide design, with focus on current capabilities and limitations. • Assessment of AI-driven tools for ASO and siRNA optimization. • Discussion of future research directions in AI-guided nucleic acid therapeutics. Small nucleic acid therapeutics, including antisense oligonucleotides (ASOs), small interfering (si)RNAs, miRNAs, and aptamers, modulate gene expression through complementary sequence recognition. Unlike traditional drugs, they can target previously inaccessible pathways and are showing promise for treating genetic, infectious, and degenerative diseases. Yet, their clinical translation is often constrained by poor stability and unpredictable pharmacokinetics. In this review, we focus on the main classes of small nucleic acid drugs, their mechanisms of action, and the chemical modifications that enhance their pharmacological properties. Importantly, we provide a critical comparison of different artificial intelligence (AI)-based methodologies for the design and optimization of oligonucleotide therapeutics, discuss their limitations (including data scarcity, off-target predictions, and clinical translation barriers), and outline concrete directions for future improvements.