DRAG: design RNAs as hierarchical graphs with reinforcement learning.

ABSTRACT: The rapid development of RNA vaccines and therapeutics puts forward intensive requirements on the sequence design of RNAs. RNA sequence design, or RNA inverse folding, aims to generate RNA sequences that can fold into specific target structures. To date, efficient and high-accuracy prediction models for secondary structures of RNAs have been developed. They provide a basis for computational RNA sequence design methods. Especially, reinforcement learning (RL) has emerged as a promising approach for RNA design due to its ability to learn from trial and error in generation tasks and work without ground truth data. However, existing RL methods are limited in considering complex hierarchical structures in RNA design environments. To address the above limitation, we propose DRAG, an RL method th

SUBMITTER: Li Y 

PROVIDER: S-EPMC11904406 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature