Pepti-drift is a one‑step peptide generation framework that uses a single latent refinement and parallel decoding, avoiding costly inference‑time guidance. It incorporates attraction toward target‑specific binders and repulsion from liability‑associated regions, achieving an 18.37% predicted Safe‑Active yield across 88 held‑out targets. The method delivers competitive gains to multi‑property‑guided baselines while reducing generation cost by 468×, enabling scalable and fair high‑throughput peptide design.
By Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Shuan Chen, Yuna Oikawa, Yiming Zhang, Gyubok Lee, Keisuke Ozawa
PepEVOLVE is a dynamic, position‑aware framework for optimizing macrocyclic peptides that learns where to edit and how to improve multiple properties. It enhances pretraining with dynamic masking and CHUCKLES shifting, employs a multi‑armed‑bandit router to identify high‑reward residues, and uses an evolving optimization algorithm with group‑relative advantage to stabilize updates. In in‑silico tests on a Rev‑binding macrocycle benchmark, PepEVOLVE surpassed PepINVENT, achieving higher permeability‑weighted scores and converging faster while respecting structural constraints.
By Trieu Nguyen, Hao-Wei Pang, Shasha Feng
Ensemble-Aware Structural Encoding (EASE) is a new framework that conditions protein encoders on peptide conformational ensembles rather than single sequences or structures. By selecting central conformers that capture a peptide’s major structural states and fusing their representations with a permutation‑invariant operator, EASE produces a peptide‑level representation that better reflects conformational variability. In evaluations on six peptide property‑prediction tasks, EASE consistently outperforms single‑structure baselines, achieving an average absolute gain of about 3.0% and improving further as more conformers are incorporated.
By Ruoyao Zhang, Hongxuan Li, Jie Peng, Tianlong Chen
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
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
Protein foundation models provide transferable representations for downstream peptide prediction tasks. However, existing representations typically reduce a peptide to a single sequence or structure,...
arXiv:2606. 07656v1 Announce Type: cross Abstract: Solubility prediction is a standard benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.
By Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sergei Tatarin, Lev Krasnov, Sayan Ranu, Tarak Karmakar
arXiv:2602. 11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties.
By Yitian Wang, Fanmeng Wang, Angxiao Yue, Wentao Guo, Yaning Cui, Hongteng Xu
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior acr...
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.
By Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Keisuke Ozawa
arXiv:2608. 19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability.
By Guofeng Zhang, Rong Han, Xiaoyu Wang, Zhiyun Li, Zongbo Han, Xiaohong Liu, Guangyu Wang
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