Aug 2026· MedComm – Biomaterials and Applications· Vol 5· 0 citations· 192 references
TL;DR
The integration of computational prediction with programmable genome and RNA engineering may support more precise, adaptable, and clinically translatable 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.
RNA therapeutics have evolved from passive genetic intermediaries into highly programmable platforms, fundamentally transforming the landscape of precision medicine. This comprehensive review examines the molecular architecture and mechanisms of established platforms in the clinical setting, including mRNA, antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs) and aptamers, alongside next-generation platforms, such as CRISPR-guided systems and circular RNAs (circRNAs). Moreover, we discuss strategies to overcome systemic delivery bottlenecks and evaluate advanced non-viral systems, emphasizing lipid nanoparticles (LNPs), polymers and tissue-specific ligand conjugates that facilitate precise intracellular targeting. Furthermore, we explore the clinical expansion of these platforms across infectious diseases, rare genetic disorders, oncology and cardiovascular conditions. Finally, we highlight how the integration of artificial intelligence (AI) and machine learning (ML) redefines the limits of individualized, programmable RNA therapies by accelerating sequence optimization and nanoparticle formulation.
Konstantina Athanasopoulou, Glykeria N. Daneva, V. Michalopoulou et al.· Current Issues in Molecular...· 0 citations
CRISPR–Cas9 has revolutionised genome editing by enabling efficient and programmable modification of defined DNA sequences, with guide RNAs (gRNAs) serving as indispensable elements that direct Cas9 to specific genomic loci. Initially regarded as auxiliary components, gRNAs are now recognized as critical determinants of editing efficiency and specificity and have attracted growing attention as independent targets for engineering. Chemical modification, sequence optimisation, and structural alteration of gRNAs have been shown to enhance on‐target activity, suppress off‐target effects and cytotoxicity, and even achieve allele‐selective precision editing in a programmable manner. Moreover, advances in artificial intelligence and machine learning have markedly improved the predictive accuracy of gRNA design through large‐scale data analysis. Despite rapid progress, a consolidated review that integrates chemical, structural, and computational advances in gRNA engineering and highlights their translational potential for therapeutic genome editing has been lacking. This review uniquely addresses that gap by presenting an integrated framework that connects molecular design principles with clinical applicability.
Masaki Kawamata, S. Niwa, Atsushi Suzuki· Chemical Biology and Drug De...· 0 citations
This review highlights the transformative impact of gene editing on cancer medicine, advancing toward more accurate, effective, and personalized therapeutic approaches.
Lipid nanoparticles (LNPs) have made RNA therapeutics clinically viable, yet delivery remains the dominant constraint on efficacy and safety as applications expand beyond the liver. Performance is emergent: it reflects coupled choices in ionizable-lipid chemistry, multi-component formulation, and process history, and is further reshaped by biological interfaces including protein corona remodeling, tissue transport barriers, endocytic trafficking, and low-probability endosomal escape. This review surveys how artificial intelligence is converting this nonlinear design space from empirical iteration into data-efficient, multi-objective optimization. We highlight (i) structure-activity learning and synthesis-aware generative modeling for ionizable lipid discovery; (ii) formulation- and process-conditioned architectures that treat LNPs as composite, process-defined materials; and (iii) pooled in vivo barcoding and single-cell readouts that enable prediction and tuning of organ and cell-type tropism. We conclude by outlining three priorities for translation: standardized data and metadata, mechanistic endpoints for escape and immunogenicity, and CMC-aware optimization. Progress on these fronts will be necessary for potent, safe, and manufacturable mRNA-LNP medicines.
Heyu Zhao, Junchao Xu, Xia Gao et al.· The Innovation Drug Discover...· 2 citations
The continued convergence of nanotechnology and genome engineering may support the development of personalized medicine strategies that adapt genetic engineering tools for patient-specific applications, thereby improving the safety and reliability of gene-editing therapies.
Raheem Mais, Ayush Kumar, Armand Ahmetaj et al.· International Journal of Mol...· 0 citations
Transcriptional regulation is a critical mechanism controlling gene expression and plays a major role in cancer, genetic disorders, and complex diseases. However, developing drugs that precisely target transcriptional processes remains challenging due to the structural complexity of transcription factors and risks of off-target effects. Recent advances in artificial intelligence (AI) have transformed drug discovery by enabling better modelling of genomic and regulatory landscapes. This review highlights AI-driven approaches in transcription modulator discovery, including in silico target identification, multi-omics integration, and structure–activity optimization. It also discusses deep learning and transformer-based genomic models for identifying disease-specific regulators and DNA elements. Furthermore, the review examines progress in developing small-molecule, epigenetic, and RNA-targeting drugs. Finally, it emphasises the importance of explainable AI and personalised therapeutics in advancing precision medicine and next-generation transcription-based drug discovery.
A. Pimpale, Priyanka S. Waghmare, Pallavi Zode et al.· Journal of Applied Pharmaceu...· 0 citations