Computational Advances in RNA Therapeutics Design and Safety

RNA bioinformatics lies at the intersection of computational biology, molecular medicine, and systems pharmacology, enabling the rational design of RNA-based therapies such as mRNA vaccines, RNA interference (RNAi) agents, and antisense oligonucleotides (ASOs). The rapid evolution of RNA therapeutics has intensified the need for computational frameworks capable of predicting RNA folding, molecular stability, delivery efficiency, and immunogenicity profiles. Despite recent breakthroughs in deep learning–based RNA structure prediction and multi-omics data integration, significant gaps persist in understanding how design parameters influence efficacy, off-target effects, and long-term safety. Current challenges include limited interpretability of complex models, incomplete representation of RNA interactions within cellular environments, and the need for computational pipelines that seamlessly integrate experimental feedback for iterative optimization.This Research Topic aims to advance the computational foundations of RNA therapeutics design and safety by promoting the development of predictive, interpretable, and scalable algorithms. Specific goals include exploring how machine learning and structural bioinformatics can accelerate candidate discovery, establishing predictive frameworks for immunogenicity and toxicity, and evaluating cross-platform reproducibility of computational predictions. Key questions include: How can computational models better capture dynamic RNA folding under physiological conditions? What features most strongly determine the safety of RNA-based drugs? How can simulation methods and data-driven inference be combined to refine design principles for therapeutic RNA?To gather further insights into computational developments supporting RNA-based drug innovation and safety evaluation, we welcome articles addressing, but not limited to, the following themes:o Novel computational models for RNA secondary and tertiary structure predictiono Machine learning approaches for the rational design of RNA therapeuticso Prediction and mitigation of off-target effects in RNA-based drugso Bioinformatics workflows for safety and immunogenicity assessmento Integration of multi-omics data for improved therapeutic designo Simulations of RNA–protein and RNA–lipid nanoparticle interactionso Dataset curation, benchmarking, and standardization for computational studieso In silico and hybrid modeling for regulatory evaluation of RNA therapeutics Topic editors: Ruby Srivastava, Shubham Choudhury, Pritee Chunarkar-Patil Source: https://www.frontiersin.org/research-topics/81544/computational-advances-in-rna-therapeutics-design-and-safety