RNA molecules have recently gained huge relevance as therapeutic targets. The direct targeting of RNA with small molecule drugs emerges for its wide applicability to different classes of RNAs. Despite this potential, the field is still in its infancy and the number of available RNA-targeted drugs remains limited. A major challenge is constituted by the highly flexible and elusive nature of the RNA targets. Nonetheless, RNA flexibility also presents unique opportunities that could be leveraged to enhance the efficacy and selectivity of newly designed therapeutic agents. To this end, computer-aided drug design techniques emerge as a natural and comprehensive approach. However, existing tools do not fully account for the flexibility of the RNA. The project of this PhD work aims to build a computational framework toward the rational design of compounds targeting RNA. The first essential step for any structure-based approach is the analysis of the available structural knowledge. However, a comprehensive, curated, and regularly updated repository for the scientific community was lacking. To fill this gap, I curated the creation of HARIBOSS ("Harnessing RIBOnucleic acid - Small molecule Structures"), a database of all the experimentally-determined structures of RNA-small molecule complexes retrieved from the PDB database. HARIBOSS is available via a dedicated web interface (https://hariboss.pasteur.cloud), and is regularly updated with all the structures resolved by X-ray, NMR, and cryo-EM, in which ligands with drug-like properties interact with RNA molecules. Each HARIBOSS entry is annotated with physico-chemical properties of ligands and RNA pockets. HARIBOSS repository, constantly updated, will facilitate the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and, ultimately, the development of in silico strategies to identify RNA-targeting small molecules. In coincidence of its release, it was possible to highlight that the majority of RNA binding pockets are unsuitable for interactions with drug-like molecules, attributed to the lower hydrophobicity and increased solvent exposure compared to protein binding sites. However, this emerges from a static depiction of RNA, which may not fully capture their interaction mechanisms with small molecules. In a broader perspective, it was necessary to introduce more advanced computational techniques for an effective accounting of RNA flexibility in the characterization of potential binding sites. In this direction, I implemented SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables the exploration of the target RNA conformational landscape through atomistic molecular dynamics. Simultaneously, it efficiently identifies RNA pockets using small probe compounds whose exploration of the RNA surface is accelerated by enhanced-sampling techniques. In a benchmark encompassing diverse large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN accurately located experimentally resolved pockets, ranking them as preferred probe hotspots. Notably, SHAMAN accuracy was superior to other tools working on static RNA structures in the realistic drug discovery scenario where only apo structures of the target are available. This establishes SHAMAN as a robust platform for future drug design endeavors targeting RNA with small molecules, especially considering its potential applicability in virtual screening campaigns. Overall, my research contributed to enhance our understanding and utilization of RNA as a target for small molecule drugs, paving the way for more effective drug design strategies in this evolving field.