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<p>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.</p>

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RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Shenggeng Lin,
  • Hong Tan,
  • Keyao Wang,
  • Ruixuan Wang,
  • Hongxia Wang,
  • Tong Zhu,
  • Yi Xiong

摘要

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.