Reinforcement learning-driven unified generative framework for multi-objective RNA codon design

Authors

Lin, S., Tan, H., Wang, K., Wang, R., Wang, H., Zhu, T., Xiong, Y.

Abstract

Current RNA codon design methods are limited by inefficient long-sequence processing and poor generalizability, often relying on a decoupled "generate-or-optimize" paradigm. We introduce RNARL, a reinforcement learning-driven framework that unifies sequence generation with multi-objective optimization. RNARL directly learns to generate high-performance sequences, effectively optimizing sequences over 3,900 nucleotides and demonstrating superior performance and universality across six species and five RNA types. RNARL thus establishes an effective and generalizable framework for RNA codon design. Finally, a user-friendly web platform is freely available to facilitate its application for RNA therapeutic design.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 15 Jun 2026.

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