arXiv cs.AI· Trieu Nguyen, Hao-Wei Pang, Shasha Feng·· 14 小时前AI 评分27
PepEVOLVE：基于组相对优势的位置感知动态多肽优化框架
PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage
AI 导读
PepEVOLVE 是一个位置感知的动态多肽先导优化框架，无需化学家预先指定可编辑位点，即可同时学习"在哪里改"和"如何优化"。它通过动态掩码与 CHUCKLES shifting 增强预训练、用无上下文多臂老虎机路由器发现高奖励残基，并结合组相对优势的进化优化算法稳定强化学习更新。
正文 · 原文
Abstract:Macrocyclic peptides are an emerging modality that combines biologics-like affinity with small-molecule-like developability, yet their vast combinatorial space and multi-parameter objectives make lead optimization slow and challenging. Prior generative approaches for peptide lead optimization (e.g., PepINVENT) require chemists to pre-specify mutable positions for optimization, choices that are not always known a priori, and rely on static pretraining and optimization algorithms that limit the model's ability to generalize and effectively optimize peptide sequences. We introduce PepEVOLVE, a position-aware, dynamic framework that learns both where to edit and how to dynamically optimize peptides for multi-parameter improvement. PepEVOLVE (i) augments pretraining with dynamic masking and CHUCKLES shifting to improve generalization, (ii) uses a context-free multi-armed-bandit router that discovers high-reward residues, and (iii) couples a novel evolving optimization algorithm with group-relative advantage to stabilize reinforcement updates. During in silico evaluations, the router policy reliably learns and concentrates probability on chemically tractable sites that influence the desired peptide's properties. On a therapeutically motivated Rev-binding macrocycle benchmark, PepEVOLVE outperformed PepINVENT by reaching higher averaged permeability-weighted optimization scores of approximately 0.8 vs. 0.6, achieving best candidates with score of 0.97 (vs. 0.89), and converging in fewer steps under the task of optimizing permeability and lipophilicity with structure constraints. Overall, PepEVOLVE offers a practical, reproducible path to linear and macrocyclic peptide lead optimization when optimal edit sites are unknown, enabling more efficient chemical space exploration and improving design quality across multiple objectives to accelerate the design cycles.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2511.16912 [cs.LG] |
| (or arXiv:2511.16912v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2511.16912 arXiv-issued DOI via DataCite |
Submission history
From: Trieu Nguyen [view email]
[v1]
Fri, 21 Nov 2025 02:51:15 UTC (1,758 KB)
[v2]
Wed, 28 Oct 2026 00:20:06 UTC (4,911 KB)
来源：arXiv cs.AI · arxiv.org