标题： 可展开抗体的引导生成 显示英文标题

标题： Guided Generation for Developable Antibodies

摘要： 治疗性抗体不仅需要高亲和力的靶点结合，还需要有利的可制造性、稳定性和安全性特征以实现临床效果。 这些特性统称为“可开发性”。 为了建立一个计算框架，用于优化抗体序列以获得有利的可开发性，我们引入了一个引导的离散扩散模型，该模型在来自观察到的抗体空间（OAS）的天然配对重链和轻链序列以及246种临床阶段抗体的定量可开发性测量数据上进行训练。 为了引导生成向生物物理可行的候选物，我们集成了一个基于软值的扩散解码（SVDD）模块，该模块在不损害自然性的前提下对采样进行偏差。 在无约束采样中，我们的模型再现了天然库和批准的治疗药物的全局特征，在SVDD引导下，我们在预测的可开发性评分上实现了相对于无引导基线的显著增强。 当与高通量可开发性检测结合使用时，该框架可以实现一种迭代的、由机器学习驱动的抗体设计流程，同时满足结合和生物物理标准。 显示英文摘要

摘要： Therapeutic antibodies require not only high-affinity target engagement, but also favorable manufacturability, stability, and safety profiles for clinical effectiveness. These properties are collectively called `developability'. To enable a computational framework for optimizing antibody sequences for favorable developability, we introduce a guided discrete diffusion model trained on natural paired heavy- and light-chain sequences from the Observed Antibody Space (OAS) and quantitative developability measurements for 246 clinical-stage antibodies. To steer generation toward biophysically viable candidates, we integrate a Soft Value-based Decoding in Diffusion (SVDD) Module that biases sampling without compromising naturalness. In unconstrained sampling, our model reproduces global features of both the natural repertoire and approved therapeutics, and under SVDD guidance we achieve significant enrichment in predicted developability scores over unguided baselines. When combined with high-throughput developability assays, this framework enables an iterative, ML-driven pipeline for designing antibodies that satisfy binding and biophysical criteria in tandem.