ArticlePLoS computational biology2018
Solving the RNA design problem with reinforcement learning.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.Nature communications · 2026Article
- codonGPT: reinforcement learning on a generative language model enables scalable mRNA design.Nucleic acids research · 2025Article
- DRAG: design RNAs as hierarchical graphs with reinforcement learning.Briefings in bioinformatics · 2025Article
- A new level of RNA-based plant protection: dsRNAs designed from functionally characterized siRNAs highly effective against Cucumber mosaic virus.Nucleic acids research · 2025Article
- Machine Learning for RNA Design: LEARNA.Methods in molecular biology (Clifton, N.J.) · 2025Article
- R3Design: deep tertiary structure-based RNA sequence design and beyond.Briefings in bioinformatics · 2024Article
- Effective Antiviral Application of Antisense in Plants by Exploiting Accessible Sites in the Target RNA.International journal of molecular sciences · 2023Article
- RNA design via structure-aware multifrontier ensemble optimization.Bioinformatics (Oxford, England) · 2023Article
- Fitness functions for RNA structure design.Nucleic acids research · 2023Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- RNA secondary structure prediction with convolutional neural networks.BMC bioinformatics · 2022Article
- Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.Nature methods · 2021Review
- MoiRNAiFold: a novel tool for complex in silico RNA design.Nucleic acids research · 2021Article
- De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.Journal of chemical information and modeling · 2020Article
- Scientific Discovery Games for Biomedical Research.Annual review of biomedical data science · 2019Article
- EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame.PLoS computational biology · 2019Article
- The current state and future directions of RNAi-based therapeutics.Nature reviews. Drug discovery · 2019Review
- Editorial focus: understanding off-target effects as the key to successful RNAi therapy.Cellular & molecular biology letters · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We use reinforcement learning to train an agent for computational RNA design: given a target secondary structure, design a sequence that folds to that structure in silico. Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length. After training it on randomly generated targets, we test it on the Eterna100 benchmark and find it outperforms all previous algorithms. Analysis of its solutions shows it has successfully learned some advanced strategies identified by players of the game Eterna, allowing it to solve some very difficult structures. On the other hand, it has failed to learn other strategies, possibly because they were not required for the targets in the training set. This suggests the possibility that future improvements to the training protocol may yield further gains in performance.
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Registered trials
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.