ArticleFrontiers in pharmacology2020
A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Harnessing Sequence Embedding and Ensemble Learning to Identify Antifungal Peptides with Low Hemolytic Risk.ACS omega · 2026Article
- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- Prediction of hemolytic peptides and their hemolytic concentration.Communications biology · 2025Article
- Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks.NPJ systems biology and applications · 2024Article
- HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides.Scientific reports · 2024Article
- Universal peptide-based potential vaccine design against canine distemper virus (CDV) using a vaccinomic approach.Scientific reports · 2024Article
- Antiplasmodial peptaibols act through membrane directed mechanisms.Cell chemical biology · 2024Article
- Antibacterial and Antiviral Properties of Chenopodin-Derived Synthetic Peptides.Antibiotics (Basel, Switzerland) · 2024Article
- AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning.BMC bioinformatics · 2022Article
- Emerging Computational Approaches for Antimicrobial Peptide Discovery.Antibiotics (Basel, Switzerland) · 2022Review
- Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity.Pharmaceuticals (Basel, Switzerland) · 2022Review
- In Silico Design of Chemically Modified Cell-Penetrating Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
- B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood-Brain Barrier Penetrating Peptides.Pharmaceutics · 2021Article
- Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.International journal of molecular sciences · 2021Article
- Current and prospective computational approaches and challenges for developing COVID-19 vaccines.Advanced drug delivery reviews · 2021Review
- Exploring the Therapeutic Potenital of the Leaderless Enterocins K1 and EJ97 in the Treatment of Vancomycin-Resistant Enterococcal Infection.Frontiers in microbiology · 2021Article
- HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.Scientific reports · 2020Article
- Potential Challenges for Coronavirus (SARS-CoV-2) Vaccines Under Trial.Frontiers in immunology · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server "HemoPImod" to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).
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