Non-coding RNAs (ncRNAs) constitute 99% of the human genome. These ncRNAs include various functional classes such as microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear and nucleolar RNAs, and long non-coding RNAs. They play crucial roles in gene expression and regulation across various biological and pathological contexts, including neuronal disorders, immune responses, cardiovascular diseases, and cancer. Consequently, ncRNAs are becoming increasingly recognized as important biomarkers for disease and potential therapeutic targets [1,2]. The pluripotent RNA function is intimately linked to its structure and dynamics [3]. Nevertheless, its plasticity poses several challenges to experimental and computational methods [4].

Computational tools can substantially boost our understanding of ncRNAs. They can capture hidden, yet significant, relationships among the structure, dynamics, and function of RNA molecules, and can complement and be integrated with experimental approaches (see, e.g. [5]). Tools ranging from atomic-level and coarse-grained models [6] to classic thermodynamic models [7] have been improved over the past decades. More recently, AI methods have entered the stage [8,9]. A mix of these methodologies can be used to model RNA structural dynamics in systems of growing complexity, ranging from small oligomers to large ncRNAs and ultimately full cells [10].

Folding of RNA has been since a long time considered to be amenable to a hierarchical approach [10]. However, the interplay between different levels of this hierarchy is significant, and the diversity of the methodologies required to study processes such as catalysis, docking with proteins, ligands or ions, and functional changes in tertiary or secondary structure, makes it virtually impossible to cover all this expertise in a single research group. Hence, a strong interplay between the communities developing and applying these techniques is fundamental. We believe that such an interplay can only be obtained by taking the challenge to organize a meeting that aims at mixing people from all these communities, forcing them to find a common language and exchange ideas on reciprocal needs.

We acknowledge that interaction with experimentalists is also fundamental in this field. However, a few meetings connecting modelers and experimentalists have been organized in the past, usually limiting the experimental and computational methods to narrow ranges. Our plan instead is to put together RNA modelers across multiple scales, thus fostering new collaborative efforts in a direction that, in our opinion, hasn’t received enough attention so far.