AI-Driven Epitranscriptomic Therapeutics: Integrating RNA Modifications into Next-Generation Drug Discovery
Keywords:
Epicode Signatures, RNA Mark Topology, AI-Driven Modomics, m6A Circuitry, RNA Writer–Reader–Eraser Network, Digital RNA Twins, CRISPR–Cas13 Epiediting, Nanopore Signal Intelligence, Predictive Transcript Engineering, Next-Gen RNA TherapeuticsAbstract
The convergence of artificial intelligence (AI) and epitranscriptomics is redefining the future landscape of therapeutic innovation by transforming how RNA modifications are decoded, interpreted, and targeted for drug discovery. Epitranscriptomic marks such as m⁶A, m⁵C, Ψ, and ac⁴C form a dynamic biochemical “RNA language” that governs transcript stability, translation, and cellular fate, yet their complexity has historically remained inaccessible to conventional molecular tools. Advanced AI frameworks—ranging from transformer-driven modification callers to generative deep-learning engines—now enable real-time identification of rare and cryptic RNA marks directly from raw nanopore signals, uncovering disease-specific epicode signatures with unprecedented resolution. These computational models further predict structural consequences of modified RNA, simulate RNA–drug interactions, and design precision inhibitors for writers, readers, and erasers such as METTL3, FTO, and YTH-domain proteins. AI-guided CRISPR–Cas13 epitranscriptome editing expands therapeutic possibilities by enabling reversible, non-genomic correction of pathogenic RNA modification patterns. Collectively, these innovations are propelling RNA-centric pharmacology beyond traditional genomics, establishing modification-aware digital RNA twins, autonomous RNA drug-design pipelines, and personalized epitranscriptomic therapies. This review synthesizes emerging breakthroughs, ongoing challenges, ethical considerations, and future directions that position AI-driven epitranscriptomic therapeutics as a transformative axis in next-generation drug discovery and precision medicine.
