Deciphering RNA–ligand binding specificity with GerNA-Bind

Yunpeng Xia, Jiayi Li, Yi-Ting Chu, Jiahua Rao, Jing Chen, Chenqing Hua, Dong-Jun Yu, Xiu-Cai Chen, Shuangjia Zheng

Abstract: RNA molecules are essential regulators of biological processes and promising therapeutic targets for various diseases. Discovering small molecules that selectively bind to specific RNA conformations remains challenging due to RNA’s structural complexity and the limited availability of high-resolution data. Here we introduce GerNA-Bind, a geometric deep learning framework to predict RNA–ligand binding specificity by integrating multistate RNA–ligand representations and interactions. GerNA-Bind achieves state-of-the-art performance on multiple benchmark datasets and excels in predicting interactions for low-homology RNA–ligand pairs. It achieves a 20.8% improvement in precision for binding-site prediction compared with AlphaFold3. Furthermore, it offers informative, well-calibrated predictions with built-in uncertainty quantification. In a large-scale virtual screening application, GerNA-Bind identified 18 structurally diverse compounds targeting the oncogenic MALAT1 RNA, with experimentally confirmed submicromolar affinities. Among them, one leading compound selectively binds the MALAT1 triple helix, reduces its transcript levels and inhibits cancer cell migration. These findings highlight GerNA-Bind’s potential as a powerful tool for RNA-focused drug discovery, offering both accuracy and biological insight. Xia et al. introduce GerNA-Bind, a geometric deep learning framework designed to predict RNA–ligand binding specificity by integrating multistate RNA–ligand interactions.

External IDs:doi:10.1038/s42256-025-01154-z