  While artificial intelligence has made remarkable strides in revealing the
relationship between biological macromolecules' primary sequence and tertiary
structure, designing RNA sequences based on specified tertiary structures
remains challenging. Though existing approaches in protein design have
thoroughly explored structure-to-sequence dependencies in proteins, RNA design
still confronts difficulties due to structural complexity and data scarcity.
Moreover, direct transplantation of protein design methodologies into RNA
design fails to achieve satisfactory outcomes although sharing similar
structural components. In this study, we aim to systematically construct a
data-driven RNA design pipeline. We crafted a large, well-curated benchmark
dataset and designed a comprehensive structural modeling approach to represent
the complex RNA tertiary structure. More importantly, we proposed a
hierarchical data-efficient representation learning framework that learns
structural representations through contrastive learning at both cluster-level
and sample-level to fully leverage the limited data. By constraining data
representations within a limited hyperspherical space, the intrinsic
relationships between data points could be explicitly imposed. Moreover, we
incorporated extracted secondary structures with base pairs as prior knowledge
to facilitate the RNA design process. Extensive experiments demonstrate the
effectiveness of our proposed method, providing a reliable baseline for future
RNA design tasks. The source code and benchmark dataset are available at
https://github.com/A4Bio/RDesign.
