MCARSMA: A Multi-Level Cross-Modal Attention Fusion Framework for Accurate RNA–Small Molecule Affinity Prediction

RNA has emerged as a critical drug target, and accurate prediction of its binding affinity with small molecules is essential for the design and screening of RNA-targeted therapeutics. Although current deep learning methods have achieved progress in predicting RNA–small molecule interactions, existing models commonly suffer from reliance on single-modality features and insufficient representation of cross-level interactions. This paper proposes a multi-level cross-modal attention fusion framework, named MCARSMA, which integrates sequence, structural, and semantic information from both RNA and small molecules. The model employs a dual-path interaction mechanism to capture multi-scale relationships spanning from atom–nucleotide fine-grained interactions to global conformational features. The model architecture comprises (1) the feature extraction of RNA secondary structure and sequence using GAT and CNN; (2) small molecule representation that combines GCN and Transformer for joint graph and sequence embedding; (3) a dual-path fusion module for atom–nucleotide fine-grained interactions and structure-guided multi-level interactions; and (4) an adaptive feature weighting mechanism implemented via a gated network. The results demonstrate that on the R-SIM dataset, MCARSMA achieves RMSE = 0.883, PCC = 0.772, and SCC = 0.773, validating the effectiveness of the proposed multi-level cross-modal attention fusion framework. This study provides a highly interpretable deep learning solution with high predictive accuracy.