一个统一模型，可生成类人抗体序列并完成多种设计任务。
IgCraft: A versatile sequence generation framework for antibody discovery and engineering
- 基于贝叶斯流网络，统一建模多种抗体生成任务。
- 在互补决定区嫁接任务中，人源性与结构保持性能达最优。
- 适合抗体发现与工程化研究者快速生成类人抗体序列。
设计更贴近自然人体抗体谱系的抗体序列是生物药开发的关键挑战。我们提出IgCraft：一种基于贝叶斯流网络的多功能配对人类抗体序列生成框架。IgCraft是首个能用单一模型完成多项抗体序列设计任务的统一生成模型，包括无条件采样、序列补全、逆折叠和互补决定区（CDR）基序支架构建。该方法在各项任务中表现优异，同时确保生成序列属于人类抗体范畴，在CDR基序嫁接任务中实现了当前最优的人源性与结构特性保留效果。通过将原本独立的任务整合到一个可扩展的生成模型中，IgCraft为抗体发现与工程化提供了灵活的序列采样平台。模型代码与权重已公开于https://github.com/mgreenig/IgCraft。
原文摘要 · Abstract (English)
Designing antibody sequences to better resemble those observed in natural human repertoires is a key challenge in biologics development. We introduce IgCraft: a multi-purpose model for paired human antibody sequence generation, built on Bayesian Flow Networks. IgCraft presents one of the first unified generative modeling frameworks capable of addressing multiple antibody sequence design tasks with a single model, including unconditional sampling, sequence inpainting, inverse folding, and CDR motif scaffolding. Our approach achieves competitive results across the full spectrum of these tasks while constraining generation to the space of human antibody sequences, exhibiting particular strengths in CDR motif scaffolding (grafting) where we achieve state-of-the-art performance in terms of humanness and preservation of structural properties. By integrating previously separate tasks into a single scalable generative model, IgCraft provides a versatile platform for sampling human antibody sequences under a variety of contexts relevant to antibody discovery and engineering. Model code and weights are publicly available at https://github.com/mgreenig/IgCraft.
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