RNA Structure

RNA structure research aims to understand and predict the complex three-dimensional arrangements of RNA molecules, crucial for their diverse biological functions. Current efforts focus on developing accurate and efficient computational methods, employing deep learning architectures like graph neural networks, transformers, and generative models (e.g., flow matching) to predict RNA secondary and tertiary structures from sequence data, often leveraging transfer learning from protein structure prediction. These advancements are significantly impacting RNA-related fields, enabling improved understanding of RNA function, design of novel RNA molecules for therapeutic applications, and more accurate analysis of RNA-protein interactions.