Deep Learning Approaches for Accurate RNA 3D Structure Prediction from Primary Sequences

This study investigates advanced deep learning techniques for predicting the three dimensional structure of RNA molecules directly from nucleotide sequences. Addressing one of the most challenging problems in computational biology, the proposed approach leverages attention based neural architectures to model complex spatial interactions in RNA, including molecules without existing structural templates. Using benchmark RNA datasets, the model is evaluated against established structural accuracy metrics and demonstrates competitive performance on complex folding tasks. The findings contribute to improved understanding of RNA structure formation and offer scalable solutions for applications in drug discovery, molecular biology, and biomedical research.