Prediction of circRNA-Drug Associations Based on Bipartite Graph Transformer

Circular RNAs (circRNAs) represent a distinctive class of non-coding RNAs with covalently closed loop structures that play crucial regulatory roles in drug response. While existing computational methods have achieved certain progress in prediction tasks, they primarily relied on circRNA genotypes and traditional molecular fingerprints, with limited utilization of multi-omics data and inadequate consideration of heterogeneous network topology. To address these limitations, this study proposed the CircRNA-Drug Bipartite Graph Transformer (CDBGT) framework to predict associations. Rather than limiting to associations between circRNA genotypes and drugs, this study integrated circRNA-drug response and target association information from multiple databases. CDBGT employed pre-trained models RNA-FM and ChemBERTa to extract features of sequence and molecular fingerprint and utilized multi-omics data to construct similarity matrices. The framework incorporated a bipartite graph transformer with topological positional encoding, comprehensively considering degree encoding, degree ranking encoding and spectral encoding to extract topological information from heterogeneous networks. Experimental results showed that CDBGT performed stably in 5-fold cross-validation. On the Response dataset, it achieved ROC-AUC of 0.9674 and PR-AUC of 0.9540, while on the Target dataset it reached ROC-AUC of 0.8621. Compared with existing methods, it showed an improvement of 3.20 to 26.87 percentage points in ROC-AUC. Ablation experiments demonstrated the necessity of each module. Through literature-supported case studies, this work suggested potential directions for circRNA-based therapeutic research.