ArticleScientific reports2020
HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.
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 70 papers.
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Who cites it
70 citing papers in PubMed.
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- PeptiVerse: A unified platform for therapeutic peptide property prediction.Nature communications · 2026Article
- Rethinking peptide developability with sequence-only models: interpretable screening of microplastic-binding peptides with gated query pooling.Chemical science · 2026Article
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- Harnessing Sequence Embedding and Ensemble Learning to Identify Antifungal Peptides with Low Hemolytic Risk.ACS omega · 2026Article
- Targeting Crimean-Congo hemorrhagic fever virus: computational and MD-driven discovery of Moringa oleifera peptides against the RdRp-embedded OTU protease.Scientific reports · 2026Article
- One-Step Design of Potent and Nonhemolytic Antimicrobial Peptides by Using a Database-Guided, Nonmachine Learning Approach.ACS infectious diseases · 2026Article
- Truncated pardaxin derivative D18.13 induces selective breast cancer apoptosis via predicted targeting of MAPK8.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction.bioRxiv : the preprint server for biology · 2026Article
- Advances in cyclotide research: bioactivity to cyclotide-based therapeutics.Molecular diversity · 2025Review
- Predicting Toxicity of Insect Venom-Derived Antimicrobial Peptides Using MD Simulations: A Comparative Study of Multi-Component and Realistic Mammalian Membrane Models.The Journal of membrane biology · 2025Article
- HEPAD: enhancing hemolytic peptide prediction with adaptive feature engineering and diverse sequence descriptors.BMC bioinformatics · 2025Article
- Peptide-Based Strategies AgainstPharmaceuticals (Basel, Switzerland) · 2025Review
- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.Current microbiology · 2025Article
- Effect of bovine milk-derived peptide on SNAP-25 of the neurotransmitter system in treating the sialorrhoea in chronic neurological diseases.Food science and biotechnology · 2025Article
- iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming.Interdisciplinary sciences, computational life sciences · 2025Article
- Enhancing Antimicrobial Peptides from Frog Skin: A Rational Approach.Biomolecules · 2025Article
- Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: molecular docking and dynamics insights.Scientific reports · 2025Article
- Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.eLife · 2025Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.
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