Towards Data-Efficient Deep Learning for RNA 3D Structure Prediction and Design

RNA 3D structure prediction is essential for understanding regulatory mechanisms, catalysis, and therapeutic RNA design, yet progress has lagged behind proteins due to limited structural data and the complexity of RNA folding. This work proposes a data-efficient, physics-informed deep learning framework for full atomistic prediction of transfer RNA (tRNA) tertiary structures directly from sequence. Our approach will integrate pretrained RNA embeddings, predicted secondary structure constraints,