The structural complexities enable RNA to serve as a versatile molecular scaffold capable of binding small molecules with high specificity. Understanding these interactions is essential for elucidating RNA's role in disease mechanisms and developing RNA-targeted therapeutics. However, predicting RNA-small molecule binding sites remains a significant challenge due to their conformational flexibility, structural diversity, and the limited availability of high-resolution structural data. In this study, we propose RLsite, a novel computational framework integrating pre-trained RNA language models with Graph Attention Networks (GAT) to predict small-molecule binding sites on RNA. Our method effectively captures both sequential and structural features of RNA by leveraging large-scale RNA sequence data to learn intrinsic patterns and processing graph-based RNA structures to highlight key topological and spatial features. Compared to existing methods, RLsite demonstrates superior accuracy, generalizability, and biological relevance, achieving a Precision of 0.749, a Recall of 0.654, an MCC of 0.474, and an AUC of 0.828 on the public test set, which significantly outperforms the previous models, such as CapBind (an AUC of 0.770), MultiModRLBP (an AUC of 0.780), and RNABind (an AUC of 0.471). Notably, a case study of the PreQ1 riboswitch has achieved strong predictive performance (AUC = 0.97, Recall = 0.9), and its predicted binding sites have been confirmed experimentally. These results underscore our method as a potentially powerful tool for RNA-targeted drug discovery and advancing our understanding of RNA-ligand interactions. The resource codes and data can be accessed at https://github.com/SaisaiSun/RLsite. Supplementary data are available at Bioinformatics online.
Sun et al. (Wed,) studied this question.