peptidy - a light-weight Python library for peptide representation in machine learning

Aug 3, 2026

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Peptides are used across domains including drug discovery and food technologies, and machine learning has become central to accelerating the search for new peptides. The library peptidy provides lightweight, user-friendly computational tooling that converts amino-acid sequence peptides into numerical representations for machine-learning workflows. It runs without external dependencies, integrates into modern Python environments, and offers multiple encoding strategies for predictive and generative models. It also handles peptides with post-translational modifications such as phosphorylation, acetylation, and methylation, extending capabilities of existing peptide and protein packages.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Abstract

Motivation

Results

Availability and implementation

Introduction

Applications of peptides

Role of machine learning in peptide discovery

Importance of peptide representation

peptidy Citation for published version (APA): Özçelik, R. , van Weesep, L. , de Ruiter, S. , & Grisoni, F. (2025) . peptidy: a light-weight Python library for peptide representation in machine learning. Bioinformatics Advances, 5(1), Article vbaf058 . [https://doi.org/10.1093/bioadv/vbaf058](https://doi.org/10.1093/bioadv/vbaf058) Document license: CC BY DOI: 10.1093/bioadv/vbaf058 Document status and date: Published: 21/03/2025 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.tue.nl/taverne](www.tue.nl/taverne) Take down policy If you believe that this document breaches copyright please contact us at: [openaccess@tue.nl](openaccess@tue.nl) providing details and we will investigate your claim. Download date: 11. Jun. 2025 Bioinformatics Advances, 2025, 00, vbaf058 [https://doi.org/10.1093/bioadv/vbaf058](https://doi.org/10.1093/bioadv/vbaf058) Advance Access Publication Date: 21 March 2025 Application Note Sequence analysis peptidy: a light-weight Python library for peptide representation in machine learning Rıza €Ozelik 1,2,􀀃 , Laura van Weesep 1, Sarah de Ruiter1, Francesca Grisoni1,2,􀀃 1 Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AZ, Netherlands 2Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrech, Utrecht 3584CB, Netherlands 􀀃 Corresponding authors. Rıza €Ozelik, Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AZ, Netherlands and Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrech, Utrecht 3584CB, Netherlands. E-mail: [r.ozcelik@tue.nl](r.ozcelik@tue.nl); Francesca Grisoni, Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AZ, Netherlands and Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrech, Utrecht 3584CB, Netherlands. E-mail: [f.grisoni@tue.nl](f.grisoni@tue.nl). Associate Editor: Alex Bateman Abstract Motivation: Peptides are widely used in applications ranging from drug discovery to food technologies. Machine learning has become increasingly prominent in accelerating the search for new peptides, and user-friendly computational tools can further enhance these efforts. Results: In this work, we introduce peptidy—a lightweight Python library that facilitate

FAQs

What problem does peptidy address in peptide machine learning workflows?

peptidy converts peptide sequences into numerical representations suitable for machine learning, helping accelerate predictive and generative approaches for peptide discovery.

What are peptidy’s key technical characteristics?

It is a lightweight Python library with no external dependencies, designed to integrate seamlessly into modern Python environments.

Does peptidy support post-translational modifications?

Yes. It supports peptides with modifications such as phosphorylation, acetylation, and methylation, extending peptide and protein encoding functionality.

Aug 3, 2026

Upload by Sage