PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
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Computer Science > Machine Learning
Title: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.
| Comments: | 18 pages, 5 figures, 3 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.14510 [cs.LG] |
| (or arXiv:2606.14510v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14510
arXiv-issued DOI via DataCite (pending registration)
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