ArticleScientific reports2021
Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- A Cell-Penetrating Peptide and Signal Sequence Fusion DNA Vaccine Enhances CD8+ T Cell-Mediated Anti-Tumor Immunity Targeting HPV-16 Cervical Cancer Antigens.Obstetrics and gynecology research · 2026Article
- SSEL-CPP: A SHAP-based feature-selection ensemble learning framework identifies molecular properties of cell-penetrating peptides.Protein science : a publication of the Protein Society · 2026Article
- CPP2Vec: A representation learning approach for cell-penetrating peptides prediction.PLoS computational biology · 2026Article
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Exploring and expanding the chemical multiverse of peptides.Chemical science · 2026Review
- Article
- Nanoparticles in the Treatment of Renal Fibrosis.International journal of nanomedicine · 2026Review
- Novel antimicrobial peptide HFIAP-1 mutant as a β-lactamase inhibitor against extended-spectrum β-lactamases of Escherichia coli: a comprehensive in-silico approach.Archives of microbiology · 2025Article
- Glycosylated 18β-glycyrrhetinic acid derivatives as promising inhibitors of the SARS-CoV-2 main protease.RSC advances · 2025Article
- EnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information.Interdisciplinary sciences, computational life sciences · 2025Article
- Research on Plant RNA-Binding Protein Prediction Method Based on Improved Ensemble Learning.Biology · 2025Article
- A BAG-1-inhibitory peptide, GO-Pep, suppresses c-Raf activity in cancer.Communications biology · 2025Article
- PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency.Bioinformatics advances · 2025Article
- Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution.PNAS nexus · 2025Article
- Article
- Article
- Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides.International journal of molecular sciences · 2024Review
- Antioxidant Peptides from Sacha Inchi Meal: An In Vitro, Ex Vivo, and In Silico Approach.Foods (Basel, Switzerland) · 2024Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyond chemical rules-based framework for CPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred .
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