Smart Design: Integrating Artificial Intelligence and Gene Editing for Advanced mRNA Therapeutics

Abstract

Artificial intelligence (AI) and gene editing are increasingly being applied to the design and evaluation of mRNA therapeutics. Although mRNA‐based medicines have achieved clear clinical impact in vaccination, broader applications remain limited by mRNA instability, delivery barriers, tissue selectivity, and unwanted immunogenicity. This review examines how AI and gene editing can be combined to address these constraints. AI‐based models support predictive optimization of untranslated regions, codon usage, secondary structure, and lipid nanoparticle (LNP) formulations, thereby improving the efficiency of sequence and delivery‐system design. In parallel, CRISPR‐Cas (clustered regularly interspaced short palindromic repeats‐associated proteins) systems, base editors, and emerging RNA‐editing tools provide platforms for disease modeling, target validation, and functional testing of mRNA‐based interventions. We emphasize that the value of this convergence lies in iterative workflows: gene‐editing screens generate quantitative datasets for model training, whereas AI helps prioritize editing strategies, guide sequence refinement, and improve delivery design. We also summarize representative applications, translational limitations, and prospects for closed‐loop AI‐gene editing platforms. Overall, the integration of computational prediction with programmable genome and RNA engineering may support more precise, adaptable, and clinically translatable mRNA therapeutics.