ToxiPep: Peptide toxicity prediction via fusion of context-aware representation and atomic-level graph

Peptide-based therapeutics have emerged as a promising avenue in drug development, offering high biocompatibility, specificity, and efficacy. However, the potential toxicity of peptides remains a significant challenge, necessitating the development of robust toxicity prediction methods. In this study, we introduce ToxiPep, a novel dual-model framework for peptide toxicity prediction that integrates sequence-based contextual information with atomic-level structural features. This framework combines BiGRU and Transformer to capture local and global sequence dependencies while leveraging multi-scale CNNs to extract refined structural features from molecular graphs derived from peptide SMILES representations. A cross-attention mechanism aligns and fuses these two feature modalities, enabling the model to capture intricate relationships between sequence and structural information. ToxiPep outperforms several state-of-the-art tools, including ToxinPred2, CSM-Toxin, PepNet, and ToxinPred3, on both internal and independent test sets. Additionally, interpretability analyses reveal that ToxiPep identifies key amino acids along with their structural features, providing insights into the molecular mechanisms of peptide toxicity. To facilitate broader accessibility, we have also developed a web server for convenient user access. Overall, this framework has the potential to accelerate the identification of safer therapeutic peptides, offering new opportunities for peptide-based drug development in precision medicine.