Pepti-drift: Toxicity-Repulsive Drifting for Antigen-Conditioned Discrete Peptide Generation
arXiv:2606.27824v1 Announce Type: new 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 a
The convergence of advanced AI techniques (drift models) with therapeutic development for peptides is maturing, driven by the increasing need for targeted and safe drug candidates.
This development represents a significant step towards automating and accelerating the design of peptide therapeutics, potentially leading to faster discovery of new treatments with reduced toxicity.
The ability to generate antigen-conditioned peptides while actively repelling toxicity introduces a new paradigm for therapeutic peptide design, improving efficacy and safety profiles at the design stage.
- · Pharmaceutical companies
- · Biotech startups
- · Patients with targeted diseases
- · AI-driven drug discovery platforms
- · Traditional drug discovery methods
- · Companies slow to adopt AI in R&D
Accelerated discovery of novel peptide therapeutics for various diseases.
Reduced R&D costs and time-to-market for effective peptide-based drugs.
A potential shift in drug development towards 'on-demand' design for highly specific and personalized therapies.
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Read at arXiv cs.LG