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...
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
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: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
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
arXiv:2608. 11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints.
By Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland, Matthew Hart, Steini Davidsson, Angelos Angelopoulos, Ron Alterovitz, Marcello DeLuca, Alexander Tropsha