English(EN) Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision

新的基于偏好的学习框架增强抗体设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-21 04:00

研究人员开发了一种新颖的基于偏好的学习框架，以改进抗体表达排名，这是抗体设计中的关键步骤。该方法利用稀缺的定量表达数据以及来自免疫数据的海量弱阳性监督数据。通过调整用于蛋白质语言模型的Direct Preference Optimization (DPO)并结合基于IMGT的对齐，该框架可以高效地在可变长度序列上进行训练。在大量内部数据集上的评估表明，该方法始终优于现有基线，为数据受限场景下优化抗体可表达性提供了可扩展的解决方案。 AI

影响这项研究通过提高抗体设计的效率，有可能加速新疗法的开发。

排序理由该集群包含一篇详细介绍特定科学任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍特定科学任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Josh Qixuan Sun, Morteza Babaie, Wenyang Hou, Mark Crowley, David Young · 2026-07-21 04:00

基于偏好的抗体表达排名：利用大规模弱监督进行扩展

arXiv:2607.16263v1 Announce Type: new Abstract: Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data. To address this, we propose a unified preference-based learning framework that i…

本文如何被排名

报道来源 [1]

基于偏好的抗体表达排名：利用大规模弱监督进行扩展

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