CocoRNA：用多智能体强化学习做RNA序列设计
CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
把RNA设计拆成多个智能体分工协作，用强化学习一起训练，在Rfam上成功率和速度都超过现有方法，做计算生物学可以看看。
arXiv 论文提出 CocoRNA，一个面向 RNA 逆设计的合作式多智能体强化学习框架。该方法把 RNA 序列设计任务分解成多个子问题，由多个智能体协作求解，训练时由集中式 critic 利用全局结构信息引导智能体联合优化。在 Rfam 数据集上，CocoRNA 在成功率和设计速度上均超过现有最先进方法，且对未见过的 RNA 结构无需额外训练即可泛化。论文还在其他生物序列设计任务上验证了框架的通用性。
CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
Designing RNA sequences that reliably fold into specific secondary structures is essential for understanding their biological functions but remains a challenging computational problem. We propose CocoRNA, a cooperative multi-agent reinforcement learning framework for RNA inverse design. CocoRNA simplifies the design task by decomposing it into smaller sub-problems, each solved collaboratively by multiple agents. This approach reduces the complexity of the problem and improves the exploration of design policies. During training, a centralized critic uses global structural information to guide the agents, enabling them to jointly optimize their design strategies. As a result, CocoRNA learns high-quality RNA design policies that generalize effectively to unseen structures without additional training. Experiments on the Rfam dataset demonstrate that CocoRNA substantially outperforms state-of-the-art methods in both success rate and design speed. Further experiments on other biological sequence design tasks highlight the effectiveness and broad potential of CocoRNA for complex design tasks.