Artificial Intelligence in RNA Research: From Molecular Design to Translational Analytics and Quality Control

Artificial intelligence is reshaping RNA research by enabling predictive, data-driven workflows across molecular design, therapeutic development, delivery optimization, and analytical quality control. RNA-based medicines, including messenger RNA vaccines, small interfering RNA therapeutics, antisense oligonucleotides, RNA-guided systems, and RNA-targeted small molecules, are programmable but structurally and analytically complex. Their performance depends not only on nucleotide sequence, but also on RNA folding, untranslated regions, chemical modifications, formulation composition, delivery efficiency, stability, manufacturability, and critical quality attributes. This review examines how AI is applied across the RNA therapeutic development pipeline, including RNA sequence and structure prediction, codon optimization, guide RNA design, neoantigen selection, RNA-targeted drug discovery, lipid nanoparticle formulation prediction, and analytical data integration. Particular emphasis is placed on AI-enabled analytics as a translational layer between computational design and therapeutic implementation. Centralized multimodal data platforms that integrate outputs from orthogonal analytical methods can transform fragmented experimental readouts into standardized, model-ready datasets. We argue that future progress will depend on closed-loop systems in which design models, delivery prediction, experimental analytics, and quality-control data continuously inform each other to improve RNA therapeutic translation.