GPepT: A Foundation Language Model for Peptidomimetics Incorporating Noncanonical Amino Acids.

Authors

Yuna Oikawa, Takanori Uzawa, Francois Berenger, Noriko Minagawa, Akiko Yumoto, Hideaki Takaku, Ryo Tamura, Yoshihiro Ito, Koji Tsuda

Published in

ACS medicinal chemistry letters. Volume 16. Issue 8. Pages 1670-1675. Aug 14, 2025. Epub Jul 22, 2025.

Abstract

Language models have been increasingly popular in therapeutic peptide generation, but molecular diversity remains limited due to reliance on the 20 canonical amino acids. We propose a language model that generates peptidomimetics incorporating noncanonical elements like noncanonical amino acids and terminal modifications. To accomplish this, we created a vocabulary of over 17,000 noncanonical elements by extracting them from chemical formulas stored in the ChEMBL database. Our pretrained language model, GPepT, showed improved diversity in molecular structures and chemical properties. To demonstrate its real-world application, we fine-tuned the model for antimicrobial peptides. Experimental validation revealed that one of the generated peptidomimetics exhibited effective antimicrobial activity, marking a successful case of AI-driven peptide development. GPepT is fully accessible on HuggingFace: https://huggingface.co/Playingyoyo/GPepT.

PMID:
40832546
Bibliographic data and abstract were imported from PubMed on 20 Aug 2025.

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