Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data

Recent advancements in RNA three-dimensional (3D) structure prediction and modeling have provided significant insights into RNA biology, highlighting the essential role of RNA in cellular functions and its potential in therapeutic applications. This review summarizes the latest developments in computational methods, focusing on the incorporation of artificial intelligence and machine learning, which have markedly improved the precision and efficiency of RNA structure predictions. Attention is given to the integration of new experimental data types, including advanced cryo-EM techniques and improvements in high-throughput sequencing, which have transformed RNA structure modeling. The combination of these experimental advances with sophisticated computational methods, especially deep learning, represents a significant leap forward in RNA structure determination. We highlight the outcomes of community-wide prediction challenges RNA-Puzzles and CASP, which assess the current state and limitations of existing methods, guiding future research directions. Future perspectives are discussed, focusing on the impact of RNA 3D structure prediction in understanding RNA mechanisms and its implications for drug discovery and RNA-targeted therapies, opening new avenues in molecular biology.