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

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

Abstract read

In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

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1 citing paper in PubMed.

RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design.Genome biology · 2026
Article

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5 · Who and what money

Authors and funding

7 authors.

Shenggeng LinState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Hong TanState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Keyao WangState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Ruixuan WangState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Hongxia WangDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.

Tong ZhuShanghai Innovation Institute, Shanghai, China. tongzhu.work@gmail.com.

Yi XiongState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. xiongyi@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

CodonRNASoftwareAlgorithmsReinforcement Machine LearningCodonRNAGenerative modelReinforcement learningRNA codon optimizationRNA vaccine

Identifiers

PMID42482120

DOI10.1186/s13059-026-04203-x

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.