arXiv — Machine Learning · · 3 min read

Pepti-drift: Toxicity-Repulsive Drifting for Antigen-Conditioned Discrete Peptide Generation

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Computer Science > Machine Learning

arXiv:2606.27824 (cs)
[Submitted on 26 Jun 2026]

Title:Pepti-drift: Toxicity-Repulsive Drifting for Antigen-Conditioned Discrete Peptide Generation

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Abstract:Peptides are a promising therapeutic modality that combine the chemical tunability of small molecules with the target specificity of macromolecular therapeutics. However, designing antigen-specific binding peptides while avoiding toxicity remains a major challenge for therapeutic peptide discovery. Here, we present Pepti-drift, a toxicity-aware latent refinement framework that generates peptide candidates through a single antigen-conditioned drift step. In a peptide embedding space, Pepti-drift learns to attract generated peptide latents toward antigen-matched binding peptides while repelling them from toxicity-associated regions. This is challenging because binding-promoting physicochemical features often overlap with toxicity-associated features in peptide representation space. To address this, we introduce a warm-up strategy to stabilize this competing objective by first learning binding-oriented attraction and then increasing toxicity repulsion.
Comments: preprint
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.27824 [cs.LG]
  (or arXiv:2606.27824v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.27824
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hikaru Shindo [view email]
[v1] Fri, 26 Jun 2026 08:04:11 UTC (3,523 KB)
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