# Learning to Design RNA

arXiv: https://arxiv.org/abs/1812.11951 · HTML: https://mlsanity.com/paper/1812.11951/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 29 of 29)

- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning](https://mlsanity.com/paper/2102.13651/) (2021)
- [Contextualize Me -- The Case for Context in Reinforcement Learning](https://mlsanity.com/paper/2202.04500/) (2022)
- [EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search](https://mlsanity.com/paper/2102.08099/) (2021)
- [CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning](https://mlsanity.com/paper/2110.02102/) (2021)
- [gRNAde: Geometric Deep Learning for 3D RNA inverse design](https://mlsanity.com/paper/2305.14749/) (2023)
- [Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization](https://mlsanity.com/paper/2105.01015/) (2021)
- [AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning](https://mlsanity.com/paper/2301.10339/) (2023)
- [Reinforcement Learning with Automated Auxiliary Loss Search](https://mlsanity.com/paper/2210.06041/) (2022)
- [Deep reinforcement learning-designed radiofrequency waveform in MRI](https://mlsanity.com/paper/2105.03061/) (2021)
- [Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL](https://mlsanity.com/paper/2106.15883/) (2021)
- [RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design](https://mlsanity.com/paper/2301.10774/) (2023)
- [BEACON: Benchmark for Comprehensive RNA Tasks and Language Models](https://mlsanity.com/paper/2406.10391/) (2024)
- [ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning](https://mlsanity.com/paper/2506.07459/) (2025)
- [Graph-based Neural Architecture Search with Operation Embeddings](https://mlsanity.com/paper/2105.04885/) (2021)
- [Fourier Representations for Black-Box Optimization over Categorical Variables](https://mlsanity.com/paper/2202.03712/) (2022)
- [Hyperparameters in Contextual RL are Highly Situational](https://mlsanity.com/paper/2212.10876/) (2022)
- [From Sentences to Sequences: Rethinking Languages in Biological System](https://mlsanity.com/paper/2507.00953/) (2025)
- [RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion](https://mlsanity.com/paper/2602.16548/) (2026)
- [Towards Automated Design of Riboswitches](https://mlsanity.com/paper/2307.08801/) (2023)
- [Rethinking Performance Measures of RNA Secondary Structure Problems](https://mlsanity.com/paper/2401.05351/) (2023)
- [Designing RNAs with Language Models](https://mlsanity.com/paper/2602.12470/) (2026)
- [Factorization Machine with Quadratic-Optimization Annealing for RNA Inverse Folding and Evaluation of Binary-Integer Encoding and Nucleotide Assignment](https://mlsanity.com/paper/2602.16643/) (2026)
- [D2R2: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction](https://mlsanity.com/paper/2608.15288/) (2026)
- [Neural representation and generation for RNA secondary structures](https://mlsanity.com/paper/2102.00925/) (2021)
- [Warm-starting DARTS using meta-learning](https://mlsanity.com/paper/2205.06355/) (2022)
- [A Survey on Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.09099/) (2023)
- [MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design](https://mlsanity.com/paper/2610.13305/) (2026)
- [TANGO: RNA Topology and Geometry Co-Design](https://mlsanity.com/paper/2610.20277/) (2026)

## Abstract (arXiv)

Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation. Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints. Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure. By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks. Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process. Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance. In an ablation study, we analyze the importance of our method's different components.