用扩散模型生成更易生产、稳定且安全的抗体序列。
Guided Generation for Developable Antibodies
- 基于自然抗体数据训练扩散模型，引导生成兼具亲和力与可开发性。
- 引入软价值解码模块，在保持自然性的前提下显著提升预测可开发性得分。
- 适合抗体药物研发人员快速筛选兼具结合与理化性能的候选分子。
治疗性抗体不仅需要高亲和力靶点结合能力，还需具备良好的可制造性、稳定性与安全性以实现临床疗效，这些特性统称为‘可开发性’。为构建优化抗体序列以获得良好可开发性的计算框架，我们引入一个基于自然配对重链与轻链序列（来自观测抗体空间OAS）及246个临床阶段抗体的定量可开发性测量数据训练的引导式离散扩散模型。为引导生成具有生物物理可行性的候选物，我们集成了一种软价值引导解码（SVDD）模块，在不牺牲序列自然性的前提下施加采样偏差。在无约束采样中，模型复现了自然抗体库与已批准疗法的全局特征；在SVDD引导下，预测可开发性得分显著优于无引导基线。结合高通量可开发性检测，该框架可实现迭代式、基于机器学习的抗体设计流程，同时满足结合与理化性质要求。
原文摘要 · Abstract (English)
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.
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