PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion

• URL: http://arxiv.org/abs/2606.14510v1
• Date: Fri, 12 Jun 2026 14:40:27 GMT
• Title: PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
• Abstract summary: We introduce PepALD, an Autoregressive Latent Diffusion model for textitde novo macrocyclic peptide generation.<n>The model represents HELM monomers with structured chemical embeddings, generates each residue through context-conditioned diffusion in chemically informed latent space.<n>In silico experiments demonstrate PepALD's generation quality and reward-optimization performance against representative peptide generation baselines.
• Score: 7.4526942053161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Macrocyclic peptides are promising therapeutic candidates for intracellular targets, but their design requires simultaneous control over non-natural monomer chemistry, ring topology, membrane permeability, and target binding. Existing SMILES- or HELM-string generative models either operate in long atom-level sequence spaces or treat monomers as symbolic tokens with limited chemical grounding. We introduce PepALD, an Autoregressive Latent Diffusion (ALD) foundation model for \textit{de novo} macrocyclic peptide generation. The model represents HELM monomers with structured chemical embeddings, generates each residue through context-conditioned diffusion in chemically informed latent space, predicts R-group-aware ring closures during autoregressive generation, and aligns the denoiser to affinity rewards using winner-protected diffusion-adapted preference optimization. In silico experiments demonstrate PepALD's generation quality and reward-optimization performance against representative peptide generation baselines.

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