PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
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 textitde 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 9 of its 26 references that are in the map. First posted on arXiv 12 Jun 2026.
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9 of 26 references.
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- 2024PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion23 Dec
Other references · 8
- 2017Attention Is All You Needcited by 59,932
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
- 2023Diffusion Model Alignment Using Direct Preference Optimizationcited by 767
- 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 270
- 2024Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positivecited by 239
- 2024PepINVENT: Generative peptide design beyond the natural amino acidscited by 2
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- 2026MacroGuide: Topological Guidance for Macrocycle Generation
- 2025PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning
- 2026PepEDiff: Zero-Shot Peptide Binder Design via Protein Embedding Diffusion
- 2025Peptide2Mol: A Diffusion Model for Generating Small Molecules as Peptide Mimics for Targeted Protein Binding
- 2026Minimal-Action Discrete Schr"odinger Bridge Matching for Peptide Sequence Design
- 2025Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
- 2025From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
- 2026FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design