RNA drug targets are pervasive in cells but methods to design small molecules that target them are sparse. Herein, we report a general approach to score the affinity and selectivity of RNA motif-small molecule interactions identified via selection. Named High Throughput Structure-Activity Relationships Through Sequencing (HiT-StARTS), HiT-StARTS is statistical in nature and compares input nucleic acid sequences to selected library members that bind a ligand via high throughput sequencing. The approach allowed facile definition of the fitness landscape of hundreds of thousands of RNA motif-small molecule binding partners. These results were mined against folded RNAs in the human transcriptome and identified an avid interaction between a small molecule and the Dicer nuclease-processing site in the oncogenic microRNA (miR)-18a hairpin precursor, which is a member of the miR-17-92 cluster. Application of the small molecule, Targapremir-18a, to prostate cancer cells inhibited production of miR-18a from the cluster, de-repressed serine/threonine protein kinase 4 protein (STK4), and triggered apoptosis. Profiling the cellular targets of Targapremir-18a via Chemical Cross Linking and isolation by Pull Down (Chem-CLIP), a covalent small molecule-RNA cellular profiling approach, and other studies showed specific binding of the compound to the miR-18a precursor, revealing broadly applicable factors that govern small molecule drugging of non-coding RNAs.Les cibles de médicament à ARN sont omniprésentes dans les cellules, mais des procédés de conception des petites molécules qui les ciblent sont rares. La présente invention concerne une approche générale pour évaluer laffinité et la sélectivité des interactions petite molécule-motif dARN identifiées par lintermédiaire dune sélection. Dénommée séquençage au moyen des relations structure-activité à haut débit (Hit-StARTS), Hit-StARTS est de nature statistique et compare les séquences dacides nucléiques en entrée à des éléments de
