ArticleACS omega2024
ToxTeller: Predicting Peptide Toxicity Using Four Different Machine Learning Approaches.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Immunoinformatics-driven multi-epitope vaccine design as a promising strategy against multidrug-resistant pathogens: a comprehensive review.Folia microbiologica · 2026Review
- Lactoferrin-Derived Peptides in Cancer Therapy: Structural Features, Mechanistic Insights and Clinical Translation Prospects.International journal of molecular sciences · 2026Review
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- Generative models for antimicrobial peptide design: auto-encoders and beyond.BioData mining · 2026Article
- Algae-Derived Peptides as Functional Food Ingredients: Bioactivities, Processing Challenges, and Computational Design Strategies.Foods (Basel, Switzerland) · 2026Review
- Exploring and expanding the chemical multiverse of peptides.Chemical science · 2026Review
- Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework.Journal of cheminformatics · 2025Article
- An innovative peptide toxicity prediction model based on multi-scale convolutional neural network and residual connection.Bioinformatics (Oxford, England) · 2025Article
- TastepepAI: An artificial intelligence platform for taste peptide de novo design.PLoS computational biology · 2025Article
- Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.Bioinformatics (Oxford, England) · 2025Article
- HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors.Journal of pharmaceutical analysis · 2025Article
- Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.Molecules (Basel, Switzerland) · 2025Article
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Authors and funding
2 authors.
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Abstract
Examining the toxicity of peptides is essential for therapeutic peptide-based drug design. Machine learning approaches are frequently used to develop highly accurate predictors for peptide toxicity prediction. In this paper, we present ToxTeller, which provides four predictors using logistic regression, support vector machines, random forests, and XGBoost, respectively. For prediction model development, we construct a data set of toxic and nontoxic peptides from SwissProt and ConoServer databases with existence evidence levels checked. We also fully utilize the protein annotation in SwissProt to collect more toxic peptides than using keyword search alone. From this data set, we construct an independent test data set that shares at most 40% sequence similarity within itself and with the training data set. From a quite comprehensive list of 28 feature combinations, we conduct 10-fold cross-validation on the training data set to determine the optimized feature combination for model development. ToxTeller's performance is evaluated and compared with existing predictors on the independent test data set. Since toxic peptides must be avoided for drug design, we analyze strategies for reducing false-negative predictions of toxic peptides and suggest selecting models by top sensitivity instead of the widely used Matthews correlation coefficient, and also suggest using a
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Registered trials
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