Dual-Modal Fusion Based on Peptide Sequence and Molecular Graph with Bidirectional Cross-Attention for Precise Peptide Toxicity Prediction

Peptide drugs have become an important direction in new drug development due to their high specificity, designability, and good biocompatibility. However, toxicity risk remains a key bottleneck limiting the translation of peptides from lead peptides to clinical applications. Therefore, there is an urgent need to develop a high-precision and scalable toxicity assessment framework. Based on the above understanding, we propose a dual-modal feature fusion framework, PTP-SMGCA, for accurate peptide toxicity discrimination. It captures local motifs and long-range dependencies through sequence pathways and characterizes atomic-level topology and bonding semantics through molecular graph pathways. By using bidirectional cross-attention to achieve dynamic alignment of molecular graph features and sequence features, consistency evidence can be automatically screened between modalities and modality-specific noise can be suppressed. Experimental results show that PTP-SMGCA significantly improves the performance of peptide toxicity prediction, with an AUROC of 0.9289 and an AUPRC of 0.9397. On the same data set, its predictive performance is better than that of several other advanced tools, including ATSE, CAPTP, ToxiPep, Toxinpred2 (AAC-based RF), and Toxinpred2 (Hybrid). Through interpretability analysis, PTP-SMGCA could effectively identify key amino acids and the key groups they form. Attention scores are read out, revealing that graph features and sequence features dynamically aligned by bidirectional cross-attention played a crucial role in our model's prediction of peptide toxicity. In summary, this peptide toxicity prediction framework enables rapid screening of toxic peptides, providing a new approach for the development of therapeutic peptide drugs.