Superposition of target structures enables design of bi-stable RNA molecules with deep learning

The ability to design RNA molecules with specific structures and functions could facilitate research and developments in biotechnology, biology and pharmacy. Here we present a flexible RNA design framework based on deep learning that locally optimizes sequences by gradient-guided search methods. We demonstrate its effectiveness by designing bi-stable RNA molecules by superimposing conformer target structures.