ArticleScientific reports2020
Machine learning-guided discovery and design of non-hemolytic peptides.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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The trial behind it
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Who cites it
65 citing papers in PubMed, 149 citations in OpenAlex.
- NToxSEM: Enhancing prediction of neurotoxic peptides and neurotoxins using a stacked ensemble-based multimodal framework.Protein science : a publication of the Protein Society · 2026Article
- Sequence-Derived and Molecular Descriptors for Interpretable Modeling of Molecular Systems: Insights from Peptide Hemolysis.Journal of chemical information and modeling · 2026Article
- Cyclotides from Plants Driving the Next Generation of Antibacterial Agents.Antibiotics (Basel, Switzerland) · 2026Review
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- Reprogrammed SimCells for antimicrobial therapy.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- HemPepPred: Quantitative Prediction of Peptide Hemolytic Activity Based on Machine Learning and Protein Language Model-Derived Features.Foods (Basel, Switzerland) · 2025Article
- A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- ConsAMPHemo: A computational framework for predicting hemolysis of antimicrobial peptides based on machine learning approaches.Protein science : a publication of the Protein Society · 2025Article
- Directed discovery of high-loading nanoaggregates enabled by drug-matched oligo-peptide excipients.Chem · 2025Article
- Enhancing Antimicrobial Peptides from Frog Skin: A Rational Approach.Biomolecules · 2025Article
- Prediction of hemolytic peptides and their hemolytic concentration.Communications biology · 2025Article
- Safety and effects of scorpion-derived antimicrobial peptides as an alternative to antibiotic growth promoters in broilers: growth performance, immune function, and intestinal development.Frontiers in veterinary science · 2025Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.BMC bioinformatics · 2024Article
- Deciphering optimal molecular determinants of non-hemolytic, cell-penetrating antimicrobial peptides through bioinformatics and Random Forest.Briefings in bioinformatics · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95-97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.
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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.