# Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models

arXiv: https://arxiv.org/abs/2408.08341 · HTML: https://mlsanity.com/paper/2408.08341/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 49 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [E(3)-invariant diffusion model for pocket-aware peptide generation](https://mlsanity.com/paper/2410.21335/) (2024)
- [PepEDiff: Zero-Shot Peptide Binder Design via Protein Embedding Diffusion](https://mlsanity.com/paper/2601.13327/) (2026)

## Abstract (arXiv)

Generating peptides with desired properties is crucial for drug discovery and biotechnology. Traditional sequence-based and structure-based methods often require extensive datasets, which limits their effectiveness. In this study, we proposed a novel method that utilized autoencoder shaped models to explore the protein embedding space, and generate novel peptide analogs by leveraging protein language models. The proposed method requires only a single sequence of interest, avoiding the need for large datasets. Our results show significant improvements over baseline models in similarity indicators of peptide structures, descriptors and bioactivities. The proposed method validated through Molecular Dynamics simulations on TIGIT inhibitors, demonstrates that our method produces peptide analogs with similar yet distinct properties, highlighting its potential to enhance peptide screening processes.