RNA plays a pivotal role in biological processes such as gene expression regulation and protein synthesis. Targeting RNA with small molecules offers a novel therapeutic strategy for various diseases by directly modulating these processes. However, the structural diversity and complexity of RNA pose significant challenges for experimentally characterizing RNA-small molecule interactions. Recently, machine learning-based approaches have emerged as powerful tools for modeling RNA-small molecule interactions, enabling accurate prediction of binding sites, poses, preferences, and affinities. This review provides a comprehensive overview of state-of-the-art machine learning algorithms designed for RNA-small molecule interaction modeling, focusing on their applications in predicting binding characteristics and their underlying mechanisms. We also highlight the limitations of current methods and systematically discuss the challenges that remain to be addressed. By advancing these computational approaches, the ultimate goal is to enable the rational design of RNA-targeted small molecule drugs with high specificity and efficacy, paving the way for novel therapeutic interventions.
Sun et al. (2025) studied this question.