Sampling-based Continuous Optimization with Coupled Variables for RNA Design

2024/12/11 by Wei Tang, Tang, Wei Yu, Ning Dai +7
Biochemistry, Genetics and Molecular Biology · #RNA and protein synthesis mechanisms #RNA Interference and Gene Delivery #Advanced biosensing and bioanalysis techniques

paper · doi:10.48550/arxiv.2412.08751

Abstract

state-of-the-art methods, our work consistently outperforms them in key metrics such as Boltzmann probability, ensemble defect, and energy gap, especially on long and hard-to-design puzzles in the Eterna100 benchmark. Our code is available at: http://github.com/weiyutang1010/ncrna_design.

Related

• Messenger RNA Design via Expected Partition Function and Continuous Optimization
• Differentiable Folding for Nearest Neighbor Model Optimization
• DesiRNA: structure-based design of RNA sequences with a replica exchange Monte Carlo approach
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning
• Target prediction and a statistical sampling algorithm for RNA-RNA interaction