The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.

European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CB10 1SD UK

Institute of Bioorganic Chemistry Polish Academy of Sciences Noskowskiego 12 14 61 704 Poznan Poland

Institute of Biotechnology of the Czech Academy of Sciences Prumyslova 595 CZ 252 50 Vestec Czech Republic

Institute of Computing Science and European Centre for Bioinformatics and Genomics Poznan University of Technology Piotrowo 2 60 965 Poznan Poland

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