Large Language Models for RNA Function: From Sequence Understanding to Functional Design

RNA molecules play central roles in nearly every aspect of cellular biology, from gene regulation and splicing to catalysis and therapeutic delivery. Yet the complexity of RNA sequence–structure–function relationships has long resisted systematic modelling. The emergence of large language models (LLMs) trained on RNA sequence data is rapidly changing this, enabling new approaches to understanding, predicting, and designing RNA function at scale.This Research Topic brings together cutting-edge work at the intersection of deep learning and RNA biology, covering both foundational model development and downstream applications.Topics of interest include, but are not limited to:- RNA foundation models - pre-training strategies, tokenisation, and architecture design for RNA sequence modelling- Structure and function prediction - secondary/tertiary structure prediction, splice site recognition, RNA–protein interaction, and regulatory element identification- Functional RNA design - generative approaches for designing aptamers, ribozymes, siRNA, mRNA therapeutics, and other functional RNA classes- Benchmarking and evaluation - datasets, metrics, and standardised evaluation frameworks for RNA LLMs- Multimodal and multi-omics integration - combining sequence models with structural, chemical, or experimental data- Biological insights from learned representations - interpretability, probing studies, and what LLMs reveal about RNA biology- Clinical and translational applications - RNA-based therapeutics, vaccine design, and disease-associated RNA variants Topic editors: Yan Gao, Chun-Jie Liu Source: https://www.frontiersin.org/research-topics/81479/large-language-models-for-rna-function-from-sequence-understanding-to-functional-design