  Our work is concerned with the generation and targeted design of RNA, a type
of genetic macromolecule that can adopt complex structures which influence
their cellular activities and functions. The design of large scale and complex
biological structures spurs dedicated graph-based deep generative modeling
techniques, which represents a key but underappreciated aspect of computational
drug discovery. In this work, we investigate the principles behind representing
and generating different RNA structural modalities, and propose a flexible
framework to jointly embed and generate these molecular structures along with
their sequence in a meaningful latent space. Equipped with a deep understanding
of RNA molecular structures, our most sophisticated encoding and decoding
methods operate on the molecular graph as well as the junction tree hierarchy,
integrating strong inductive bias about RNA structural regularity and folding
mechanism such that high structural validity, stability and diversity of
generated RNAs are achieved. Also, we seek to adequately organize the latent
space of RNA molecular embeddings with regard to the interaction with proteins,
and targeted optimization is used to navigate in this latent space to search
for desired novel RNA molecules.
