Identifying functional sites of RNA, particularly those where small molecules bind, is crucial for understanding related biological processes and advancing drug design. Small molecule therapies, compared to traditional protein-targeted therapies, have the potential to pioneer novel RNA-specific therapeutic strategies. However, the challenge lies in developing accurate and efficient computational methods, requiring novel computational models that can better characterize RNA and precisely predict RNA-small molecule binding sites. In this study, we introduced GATRsite, an efficient deep learning framework leveraging graph attention networks (GATs) and Pretrained RNA Language Models to predict RNA-ligand binding sites. GATRsite regards RNA nucleotides as nodes, and its main component is an RNA graph with nodes that comprehensively incorporates both sequential and structural features. Furthermore, it integrates embeddings derived from advanced Pretrained RNA Language Models, which precisely capture the intricate structural and functional complexities of RNA molecules. GATRsite outperforms other state-of-the-art methods, particularly in terms of recall rates, Matthew's correlation coefficient, and F1 score on benchmark test sets. Moreover, GATRsite exhibits significant robustness regarding the predicted RNA structures. A user-friendly online server for GATRsite is freely available at https://malab.sjtu.edu.cn/GATRsite/.
Sun et al. (Thu,) studied this question.