{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-to-design-rna","title":"Learning to Design RNA","arxiv_id":"1812.11951","date":"2018-12-31","proceeding":"ICLR 2019 5","authors":["Frederic Runge","Danny Stoll","Stefan Falkner","Frank Hutter"],"abstract":"Designing RNA molecules has garnered recent interest in medicine, synthetic\nbiology, biotechnology and bioinformatics since many functional RNA molecules\nwere shown to be involved in regulatory processes for transcription,\nepigenetics and translation. Since an RNA's function depends on its structural\nproperties, the RNA Design problem is to find an RNA sequence which satisfies\ngiven structural constraints. Here, we propose a new algorithm for the RNA\nDesign problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train\na policy network to sequentially design an entire RNA sequence given a\nspecified target structure. By meta-learning across 65000 different RNA Design\ntasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA\nDesign policy that can be applied out of the box to solve novel RNA Design\ntasks. Methodologically, for what we believe to be the first time, we jointly\noptimize over a rich space of architectures for the policy network, the\nhyperparameters of the training procedure and the formulation of the decision\nprocess. Comprehensive empirical results on two widely-used RNA Design\nbenchmarks, as well as a third one that we introduce, show that our approach\nachieves new state-of-the-art performance on the former while also being orders\nof magnitudes faster in reaching the previous state-of-the-art performance. In\nan ablation study, we analyze the importance of our method's different\ncomponents.","url_abs":"http://arxiv.org/abs/1812.11951v2","url_pdf":"http://arxiv.org/pdf/1812.11951v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-to-design-rna","repo_url":"https://github.com/automl/learna","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"learning-to-design-rna","repo_url":"https://github.com/2023-MindSpore-4/Code14/tree/main/RNA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"learning-to-design-rna","repo_url":"https://github.com/2023-MindSpore-4/Code6/tree/main/RNA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"learning-to-design-rna","repo_url":"https://github.com/MindSpore-paper-code-3/code5/tree/main/RNA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"learning-to-design-rna","repo_url":"https://github.com/code-implementation1/Code7/tree/main/RNA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[{"slug":"rnadesign","name":"RNADesign","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.11951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.11951"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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