Article

Beyond natural amino acids: Extending immunogenicity risk assessment to non-canonical peptide drugs through chemical feature encoding

2026-05-26

Abstract excerpt

Peptide therapeutics are increasingly used to treat challenging diseases, but immunogenicity risks limit their clinical success. In silico tools enable immunogenicity screening through prediction of peptide-MHCII binding, yet current methods fail to capture chemical properties of non-natural amino acids routinely incorporated to improve drug properties. Here, we present a machine learning approach combining chemi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bfd25e42-8698-5bb0-b37a-2de4a6187a07
DOI: 10.64898/2026.05.22.727138

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Beyond natural amino acids: Extending immunogenicity risk assessment to non-canonical peptide drugs through chemical feature encodingDOI 10.64898/2026.05.22.727138Read

Peptide-HLA II interaction prediction for post-translationally modified peptidesSemantic proximity Read

A structure-informed deep learning framework for modeling TCR-peptide-HLA interactionsSemantic proximity Read

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201Semantic proximity Read

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201Semantic proximity Read

Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.Semantic proximity Read

Learning a peptide-protein binding affinity predictor with kernel ridge regression.Semantic proximity Read

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopesSemantic proximity Read

RareFold: Structure Prediction and Design of Proteins with Noncanonical Amino AcidsSemantic proximity Read

Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests.Semantic proximity Read

In silico immunogenicity assessment for sequences containing unnatural amino acids: A method using existing in silico algorithm infrastructure and a vision for future enhancementsSemantic proximity Read

Antibody immunogenicity prediction and optimization with ImmunoSeqSemantic proximity Read

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.Semantic proximity Read