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Learning to Design RNA
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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.
Forward citations
Cited by 5 Pith papers
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Auditing Discovery Claims: A Two-Sided Criterion for Agentic Science, with the Negative Side Decidable
A two-sided audit with a formally decidable negative side shows a single-oracle RNA design claim collapses from 43/60 to 1/60 under a three-predictor panel, while two AI-written operators survive a held-out judge.
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Factorization Machine with Quadratic-Optimization Annealing for RNA Inverse Folding and Evaluation of Binary-Integer Encoding and Nucleotide Assignment
In RNA inverse folding, FMQA with one-hot or domain-wall encoding finds lower-defect sequences with fewer evaluations than binary/unary encodings and than TPE, GA, and random search on the tested benchmarks.
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From Sentences to Sequences: Rethinking Languages in Biological System
A new RNA inverse folding model (RiFold) using stochastic-order decoding and structure-aware metrics outperforms prior methods, and the paper shows sequence recovery and structural recovery are correlated but not equivalent.
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Efficient design of rna sequences with desired properties, structure, and motifs using a grammar variational autoencoder
RGVAE, a stochastic-context-free-grammar VAE for RNA, can generate sequences satisfying design constraints, but the claimed outperformance over baselines is not convincingly demonstrated.
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Spiking Neural Network Architecture Search: A Survey
A survey of Spiking Neural Network architecture search techniques viewed through a hardware/software co-design lens.
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