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Structure of PeptidoglycanPeptidoglycan is a vital structural component of the bacterial cell wall, providing mechanical strength and shape to the cell. It consists of repeating units of two sugars—N-acetylglucosamine (NAG) and N-acetylmuramic acid (NAM)—linked by β-1,4 glycosidic bonds. These sugar chains are cross-linked by short peptide chains, forming a mesh-like polymer that surrounds the bacterial plasma membrane.Cytoplasmic Phase – Precursor SynthesisPeptidoglycan...
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GPepT: A Foundation Language Model for Peptidomimetics Incorporating Noncanonical Amino Acids.

Yuna Oikawa1, Takanori Uzawa2,3, Francois Berenger1

  • 1Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan.

ACS Medicinal Chemistry Letters
|August 20, 2025
PubMed
Summary

This study introduces GPepT, an AI language model that generates diverse peptidomimetics using noncanonical amino acids. One generated molecule showed promising antimicrobial activity, advancing AI-driven drug discovery.

Keywords:
AIAmino AcidsAntimicrobial PeptidesGPTLanguage ModelNoncanonical Amino AcidsPeptidePeptidomimeticsProteinRDKitSMILES

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Peptide science

Background:

  • Current language models for peptide generation are limited by the 20 canonical amino acids, restricting molecular diversity.
  • Therapeutic peptide development requires exploring novel chemical spaces beyond standard amino acid sequences.

Purpose of the Study:

  • To develop a language model capable of generating peptidomimetics with enhanced molecular diversity.
  • To incorporate noncanonical amino acids and terminal modifications into AI-generated peptide structures.
  • To demonstrate the utility of the model in designing functional peptides, such as antimicrobial agents.

Main Methods:

  • Created a large vocabulary of over 17,000 noncanonical elements from chemical formulas in the ChEMBL database.
  • Developed and pretrained a novel language model, named GPepT, for peptidomimetic generation.
  • Fine-tuned GPepT for the specific task of designing antimicrobial peptides.

Main Results:

  • The GPepT language model demonstrated improved diversity in generated molecular structures and chemical properties compared to existing methods.
  • Experimental validation confirmed that a peptidomimetic designed by GPepT exhibited significant antimicrobial activity.
  • Successfully showcased a practical application of AI in discovering novel therapeutic peptides.

Conclusions:

  • GPepT significantly expands the scope of AI-driven peptide generation by incorporating noncanonical elements.
  • The model represents a successful advancement in creating diverse and potentially functional peptidomimetics.
  • This work highlights the potential of AI in accelerating the discovery of new antimicrobial agents and other therapeutics.