Solving the RNA design problem with reinforcement learning

Abstract

We use reinforcement learning to train an agent for computational RNA design: given a target secondary structure, design a sequence that folds to that structure in silico. Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length. After training it on randomly generated targets, we test it on the Eterna100 benchmark and find it outperforms all previous algorithms. Analysis of its solutions shows it has successfully learned some advanced strategies identified by players of the game Eterna, allowing it to solve some very difficult structures. On the other hand, it has failed to learn other strategies, possibly because they were not required for the targets in the training set. This suggests the possibility that future improvements to the training protocol may yield further gains in performance.

Author summary

Designing RNA sequences that fold to desired structures is an important problem in bioengineering. We have applied recent advances in machine learning to address this problem. The computer learns without any human input, using only trial and error to figure out how to design RNA. It quickly discovers powerful strategies that let it solve many difficult design problems. When tested on a challenging benchmark, it outperforms all previous algorithms. We analyze its solutions and identify some of the strategies it has learned, as well as other important strategies it has failed to learn. This suggests possible approaches to further improving its performance. This work reflects a paradigm shift taking place in computer science, which has the potential to transform computational biology. Instead of relying on experts to design algorithms by hand, computers can use artificial intelligence to learn their own algorithms directly. The resulting methods often work better than the ones designed by humans.