Harnessing Computational Approaches for RNA-Targeted Drug Discovery

RNA molecules have emerged as promising therapeutic targets due to their diverse functional and regulatory roles within cells. Computational modeling in RNA-targeted drug discovery presents a significant opportunity to expedite the discovery of novel small molecule compounds. However, this field encounters unique challenges compared to protein-targeted drug design, primarily due to limited experimental data availability and current models' inability to adequately address RNA's conformational flexibility during ligand recognition. Despite these challenges, several studies have successfully identified active RNA-targeting compounds using structure-based approaches or quantitative structure-activity relationship (QSAR) models. This review offers an overview of recent advancements in modeling RNA-small molecule interactions, emphasizing practical applications of computational methods in RNA-targeted drug discovery. Additionally, we survey existing databases that catalog nucleic acid-small molecule interactions. As interest in RNA-small molecule interactions grows and curated databases expand, the field anticipates rapid development. Novel computational models are poised to enhance the identification of potent and selective small-molecule modulators for therapeutic needs.