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

RNA 3D structure prediction remains a fundamental challenge due to limited experimental data, conformational heterogeneity, and complex folding landscapes. Inspired by breakthroughs in protein modeling, a data-efficient deep learning framework that integrates RNA language embeddings, geometric constraints, and physics-informed priors is proposed. This approach leverages self-supervised pretraining, contrastive learning, and SE(3)-equivariant architectures to capture higher-order structural relationships from scarce data. Predicted structures are evaluated using benchmark datasets, thermodynamic plausibility, and docking-based functional assessments, ensuring both structural accuracy and biophysical relevance. By advancing RNA structure prediction and design, this work aims to accelerate the development of RNA-based therapeutics, catalytic ribozymes, and precision medicine applications, while providing an open-source framework for the broader scientific community.