  Solving the RNA inverse folding problem is a critical prerequisite to RNA
design, an emerging field in bioengineering with a broad range of applications
from reaction catalysis to cancer therapy. Although significant progress has
been made in developing machine-based inverse RNA folding algorithms, current
approaches still have difficulty designing sequences for large or complex
targets. On the other hand, human players of the online RNA design game EteRNA
have consistently shown superior performance in this regard, being able to
readily design sequences for targets that are challenging for machine
algorithms. Here we present a novel approach to the RNA design problem,
SentRNA, a design agent consisting of a fully-connected neural network trained
end-to-end using human-designed RNA sequences. We show that through this
approach, SentRNA can solve complex targets previously unsolvable by any
machine-based approach and achieve state-of-the-art performance on two separate
challenging test sets. Our results demonstrate that incorporating human design
strategies into a design algorithm can significantly boost machine performance
and suggests a new paradigm for machine-based RNA design.
