DistRMI: a deep distance-aware neural network for explainable RNA loop motif-small molecule interaction prediction

RNA participates in the occurrence and development of various diseases by regulating gene expression. Owing to its potential to circumvent the limitations of traditional "undruggable" protein targets, it is regarded as a core direction for next-generation precision therapy. Against this backdrop, the accurate and interpretable prediction of RNA-small molecule interactions has become a key link in accelerating the discovery of RNA-targeted drugs. However, existing methods suffer from insufficient prediction accuracy and interpretability, failing to effectively guide lead compound screening or elucidate the mechanism of action. This study presents DistRMI, which integrates Transformers and graph neural networks to capture, respectively, the sequence information of RNA loop motifs and the chemical topological features of small molecules while introducing distance priors between them and leveraging a distance-aware attention mechanism to capture their interaction information. The results show that DistRMI outperforms baseline models, and its performance remains robust even when confronted with unknown RNA loop motifs and small molecules. Visualization of attention weights reveals that bases near the paired bases of RNA loop motifs contribute significantly. Furthermore, retrospective case studies validate the model's reliability. Predicting the binding preferences between RNA loop motifs and small molecules while providing interpretability facilitates an in-depth understanding of RNA-small molecule interactions, promotes in-depth research on RNA and related drugs, and opens up new avenues for disease treatment.