PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion

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.

Machine Learning (cs.LG)

arXiv:2606.14510 [cs.LG]

(or arXiv:2606.14510v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.14510 Focus to learn more arXiv-issued DOI via DataCite

supplementary_data.pdf

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