arXiv AI June 29, 2026 By Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Keisuke Ozawa

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

arXiv:2606. 27824v1 Announce Type: cross Abstract: Peptides are a promising therapeutic modality that combine the chemical tunability of small molecules with the target specificity of macromolecular therapeutics.

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 AI.

arXiv AI

2d ago

Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance

arXiv:2606.27824v3 Announce Type: replace-cross Abstract: Therapeutic peptides are a promising drug modality, but their generation must satisfy multiple therapeutic constraints. We introduce BindSafe...

By Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Shuan Chen, Yuna Oikawa, Yiming Zhang, Gyubok Lee, Keisuke Ozawa

efficiency benchmarks safety

arXiv Machine Learning

Jun 15

PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion

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.

By Junming Zhang, Siyu Yi, Wei Ju, Zhonghui Gu

rag diffusion

arXiv AI

Aug 25

PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis

arXiv:2608.21367v1 Announce Type: cross Abstract: Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on...

By Hao Qian, Shikui Tu, Lei Xu

llms rag fine-tuning

arXiv AI

Sep 10

Partner-aware Peptide-Protein Interaction Prediction and Target-conditioned Peptide Generation

arXiv:2604.18467v3 Announce Type: replace-cross Abstract: Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterizat...

By Chupei Tang, Junxiao Kong, Moyu Tang, Di Wang, Jixiu Zhai, Ronghao Xie, Shangkun Sima, Tianchi Lu

benchmarks

arXiv AI

Jun 12

APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization

arXiv:2606. 12991v1 Announce Type: new Abstract: Cyclic peptides represent a promising class of therapeutic compounds in modern drug discovery, often offering improved stability and binding affinity.

By Yifan Zhao, Lang Qin, Jintai Chen

arXiv Machine Learning

1d ago

SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems

SupraTITO is a transferable generative molecular dynamics framework designed for supramolecular systems, specifically peptide self‑assembly. It learns implicit transfer operators conditioned on peptide sequence, molecular topology, and periodic geometry, enabling the propagation of configurations over time intervals far longer than a single MD step. On a dipeptide benchmark, SupraTITO generalizes to unseen sequences, accurately reproduces sequence‑dependent structures and dynamics, and maintains molecular integrity over long rollouts, outperforming direct ensemble prediction models.

By Weilong Chen, Nuno Costa, Julija Zavadlav

benchmarks

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Related stories

Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance

PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion

PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis

Partner-aware Peptide-Protein Interaction Prediction and Target-conditioned Peptide Generation

APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization

SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems