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Deciphering optimal molecular determinants of non-hemolytic, cell-penetrating antimicrobial peptides through bioinformatics and Random Forest

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/anie.201510518openalex-snapshot

An Intrinsically Disordered Peptide Facilitates Non‐Endosomal Cell Entry

inferredcites →https://doi.org/10.1002/pep2.24125openalex-snapshot

The effect of turn residues on the folding and cell‐penetrating activity of β‐hairpin peptides and applications toward protein delivery

inferredcites →https://doi.org/10.1007/978-1-4939-2806-4_3openalex-snapshot

Prediction of Cell-Penetrating Peptides

inferredcites →https://doi.org/10.1007/s11274-023-03667-4openalex-snapshot

Combating fungal phytopathogens with human salivary antimicrobial peptide histatin 5 through a multi-target mechanism

inferredcites →https://doi.org/10.1016/j.bbamem.2006.02.001openalex-snapshot

Molecular mechanism of antimicrobial peptides: The origin of cooperativity

inferredcites →https://doi.org/10.1016/j.bpj.2019.03.033openalex-snapshot

Lipid Bilayer Composition Influences the Activity of the Antimicrobial Peptide Dermcidin Channel

inferredcites →https://doi.org/10.1016/j.colsurfb.2016.02.003openalex-snapshot

Role of amphipathicity and hydrophobicity in the balance between hemolysis and peptide–membrane interactions of three related antimicrobial peptides

inferredcites →https://doi.org/10.1021/acs.accounts.6b00074openalex-snapshot

How Membrane-Active Peptides Get into Lipid Membranes

inferredcites →https://doi.org/10.1021/acs.jproteome.7b00019openalex-snapshot

CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency

inferredcites →https://doi.org/10.1021/acs.jproteome.8b00148openalex-snapshot

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy

inferredcites →https://doi.org/10.1021/bi048206qopenalex-snapshot

Membrane Translocation Mechanism of the Antimicrobial Peptide Buforin 2

inferredcites →https://doi.org/10.1021/ci4007003openalex-snapshot

Prediction of Linear Cationic Antimicrobial Peptides Based on Characteristics Responsible for Their Interaction with the Membranes

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1038/nbt1012openalex-snapshot

Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins

inferredcites →https://doi.org/10.1038/s41573-019-0058-8openalex-snapshot

Antimicrobial host defence peptides: functions and clinical potential

inferredcites →https://doi.org/10.1038/s41598-020-67701-3openalex-snapshot

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks

inferredcites →https://doi.org/10.1038/s41598-020-73644-6openalex-snapshot

Machine learning-guided discovery and design of non-hemolytic peptides

inferredcites →https://doi.org/10.1038/srep22843openalex-snapshot

A Web Server and Mobile App for Computing Hemolytic Potency of Peptides

inferredcites →https://doi.org/10.1038/srep42362openalex-snapshot

Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC

inferredcites →https://doi.org/10.1074/jbc.m111.303602openalex-snapshot

Roles of Hydrophobicity and Charge Distribution of Cationic Antimicrobial Peptides in Peptide-Membrane Interactions

inferredcites →https://doi.org/10.1080/00401706.1964.10490181openalex-snapshot

Multiple Comparisons Using Rank Sums

inferredcites →https://doi.org/10.1080/01621459.1952.10483441openalex-snapshot

Use of Ranks in One-Criterion Variance Analysis

inferredcites →https://doi.org/10.1093/bioinformatics/btaa160openalex-snapshot

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

inferredcites →https://doi.org/10.1093/bioinformatics/btn392openalex-snapshot

HELIQUEST: a web server to screen sequences with specific α-helical properties

inferredcites →https://doi.org/10.1093/bioinformatics/bty179openalex-snapshot

Deep learning improves antimicrobial peptide recognition

inferredcites →https://doi.org/10.1093/biomet/52.3-4.591openalex-snapshot

An Analysis of Variance Test for Normality (Complete Samples)

inferredcites →https://doi.org/10.1093/database/bau051openalex-snapshot

ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures

inferredcites →https://doi.org/10.1093/nar/gkaa991openalex-snapshot

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

inferredcites →https://doi.org/10.1093/nar/gkab651openalex-snapshot

DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides

inferredcites →https://doi.org/10.1093/nar/gkac933openalex-snapshot

CAMPR4: a database of natural and synthetic antimicrobial peptides

inferredcites →https://doi.org/10.1093/nar/gkv1266openalex-snapshot

CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides

inferredcites →https://doi.org/10.1128/aac.02340-16openalex-snapshot

Intracellular Targeting Mechanisms by Antimicrobial Peptides

inferredcites →https://doi.org/10.1186/1471-2105-7-123openalex-snapshot

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

inferredcites →https://doi.org/10.1186/1471-2105-8-65openalex-snapshot

AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

inferredcites →https://doi.org/10.1214/aoms/1177729437openalex-snapshot

Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes

inferredcites →https://doi.org/10.1214/aoms/1177730256openalex-snapshot

Table for Estimating the Goodness of Fit of Empirical Distributions

inferredcites →https://doi.org/10.1371/journal.pcbi.1002101openalex-snapshot

Prediction of Cell Penetrating Peptides by Support Vector Machines

inferredcites →https://doi.org/10.1371/journal.pone.0016968openalex-snapshot

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

inferredcites →https://doi.org/10.1385/1-59259-890-0:571openalex-snapshot

Protein Identification and Analysis Tools on the ExPASy Server

inferredcites →https://doi.org/10.2147/dddt.s191072openalex-snapshot

Structure–activity relationship of an antimicrobial peptide, Phylloseptin-PHa: balance of hydrophobicity and charge determines the selectivity of bioactivities

inferredcites →https://doi.org/10.2174/157340910791202478openalex-snapshot

Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

inferredcites →https://doi.org/10.3390/ijms22094509openalex-snapshot

Aggregation and Its Influence on the Bioactivities of a Novel Antimicrobial Peptide, Temporin-PF, and Its Analogues

inferredcites →https://doi.org/10.3390/ijms242316723openalex-snapshot

Enhancing the Antimicrobial Properties of Peptides through Cell-Penetrating Peptide Conjugation: A Comprehensive Assessment

inferredcites →https://doi.org/10.48550/arxiv.2010.16061openalex-snapshot

Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← backgroundhttps://doi.org/10.5281/zenodo.21469239zenodo-depositor-declared

inferred← citeshttps://doi.org/10.1007/978-981-92-3716-6_27openalex-snapshot

Motif-Aware Graph Attention Networks for Hemolytic Peptide Prediction

inferred← citeshttps://doi.org/10.1016/j.aca.2026.345991openalex-snapshot

PepOSX-AI: CPP - an interpretable transformer-based deep learning model for prediction of cell-penetrating peptides

inferred← citeshttps://doi.org/10.1016/j.fbio.2026.108438openalex-snapshot

In silico discovery of a novel sheep whey protein-derived antimicrobial peptide: antimicrobial activity, mechanism of action, and application

inferred← citeshttps://doi.org/10.1016/j.jfutfo.2026.08.027openalex-snapshot

Accelerating the Discovery of Antimicrobial Peptides against Foodborne Pathogens via a Stacked Ensemble Learning Framework with Multimodal Feature Filtering

inferred← citeshttps://doi.org/10.1016/j.jpha.2026.101797openalex-snapshot

The isoelectric point as a design parameter: From charge characterization to programmable biomedicine

inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01031openalex-snapshot

Sequence-Derived and Molecular Descriptors for Interpretable Modeling of Molecular Systems: Insights from Peptide Hemolysis

inferred← citeshttps://doi.org/10.3389/fddsv.2026.1820604openalex-snapshot

Toward explainable and adaptive artificial intelligence systems for antimicrobial peptide discovery under resistance pressure

inferred← citeshttps://doi.org/10.3389/fmicb.2026.1670569openalex-snapshot

Hybrid and conjugated antimicrobial peptides: new tactics to counter bacterial resistance

inferred← citeshttps://doi.org/10.3390/futurepharmacol6020032openalex-snapshot

Antimicrobial Peptides Against ESKAPE Pathogens: Mechanisms, Molecular Optimization, and Current Limitations

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