Cooperative AI agents improve RNA sequence design speed and success
CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
Machine LearningMultiagent Systems
Summary
Designing RNA molecules that fold into specific shapes is important for biology but hard to do with computers. The authors propose CocoRNA, a system where many AI agents work together to design RNA sequences piece by piece. These agents learn from a shared guide that considers the whole RNA shape, helping them improve their strategies. Tests show CocoRNA works faster and better than other methods and can handle new RNA shapes without extra training.
What this means in practice
- •For synthetic biologists: Design RNA sequences that fold into desired shapes more reliably and quickly for synthetic biology experiments.
- •For biotech software developers: Incorporate a collaborative multi-agent reinforcement learning approach to improve automated RNA design tools for research and development.
Authors
Tianmeng Hu, Biao Luo, Ke Li
Abstract
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.