arXiv Machine Learning June 15, 2026 By Junming Zhang, Siyu Yi, Wei Ju, Zhonghui Gu

PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion

Read the original on arXiv Machine Learning →

arXiv:2606. 14510v1 Announce Type: new 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models from linear to cyclic...

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Scalable Peptide Design via Memory-Efficient Equivariant Transformer

arXiv:2606. 25006v1 Announce Type: new Abstract: Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints.

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Pepti-drift: Toxicity-Repulsive Drifting for Antigen-Conditioned Discrete Peptide Generation

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