提出新算法，更快更优设计RNA序列。
The Montparnasse Algorithm for RNA Design
- 基于蒙特卡洛搜索与自适应策略，融合领域先验知识。
- 100个基准测试全胜，速度比上一代快3倍以上。
- 适合合成生物学、药物开发等需要高效RNA设计的场景。
RNA设计旨在寻找满足预设条件（如二级结构）的核苷酸序列，对合成生物学、医学和纳米技术具有重要意义。本文提出Montparnasse算法，基于广义嵌套滚动策略适应的蒙特卡洛搜索框架，引入问题特定先验知识，一级缓慢长周期适应，并采用字典序多目标评估。在Eterna100 V1基准的全部100个谜题中，Montparnasse在所有时间限制下均一致优于DesiRNA（此前最优方法），整体实现完整覆盖速度快逾三倍。在血红蛋白α信使RNA二级结构优化任务中，该算法找到的序列配对碱基数超过LinearDesign的MFE最优解。
原文摘要 · Abstract (English)
RNA design consists of discovering a nucleotide sequence that optimizes predefined criteria, such as secondary structure. It is useful for synthetic biology, medicine, and nanotechnology. We propose Montparnasse, a Monte Carlo search framework based on Generalized Nested Rollout Policy Adaptation, augmented with a problem-specific prior, slow and long adaptation at level 1, and a lexicographic multicriteria evaluation. Montparnasse solves all 100 puzzles of the Eterna100 V1 benchmark consistently faster than DesiRNA, the previous state of the art, across all time limits, reaching full coverage more than three times faster overall. On messenger RNA secondary structure optimization for hemoglobin alpha, it identifies sequences with more paired bases than the MFE-optimal solution of LinearDesign.
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