ArticleComputational and structural biotechnology journal2025
ToxiPep: Peptide toxicity prediction via fusion of context-aware representation and atomic-level graph.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.Molecular diversity · 2026Article
- ToxMamba: a novel method for toxic peptide prediction based on the fusion of pretrained embeddings and multi-scale state space models.Briefings in bioinformatics · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Review
- Structural Assessment of Fibrillated Beta Amyloid to Reveal the Potential Epitopes for Alzheimer's Disease.Cell biochemistry and biophysics · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- Autophagy: mechanisms, roles in human diseases, and therapeutic perspectives.Frontiers in cell and developmental biology · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- UNKAI: A Protein Functional Identity Prediction Model Based on ESM-C Latent Representations and the Attention Mechanism.Computational and structural biotechnology journal · 2026Article
- The role of the cardiac lymphatic system in heart failure "reverse remodeling": from developmental signals to druggable targets.Frontiers in immunology · 2026Review
- Short Peptides as Excipients in Parenteral Protein Formulations: A Mini Review.Pharmaceutics · 2025Review
- Exo-Tox: Identifying Exotoxins from secreted bacterial proteins.BioData mining · 2025Article
- Uncovering the potential virulence factors of emerging pathogens using AI/ML-based tools: a case study inFrontiers in cellular and infection microbiology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.