用共享隐空间黑箱引导，高效设计抗体序列与结构。
Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
- 在共享隐空间中联合优化抗体序列与结构，提升设计同步性。
- 黑箱策略降低查询次数50%，同时超越基线方法的属性优化效果。
- 适合需多属性协同优化的抗体药物研发场景。
深度生成模型的进步使得在抗原-抗体复合物背景下实现抗体序列与结构的联合建模成为可能。然而，现有优化互补决定区（CDRs）以提升可开发性的方法在原始数据空间中进行，导致评估成本过高，搜索效率低下。为此，我们提出LatEnt blAck-box Design（LEAD）框架，在共享隐空间中同时优化序列与结构。优化共享隐码不仅突破了现有方法的局限，还能保证不同模态设计的一致性。特别地，我们设计了黑箱引导策略，适用于许多不可微分的属性评估器的实际场景。实验结果表明，LEAD在单目标与多目标优化上均表现更优。值得注意的是，LEAD将查询次数减少一半，同时在属性优化上超过基线方法。代码已开源：https://github.com/EvaFlower/LatEnt-blAck-box-Design。
原文摘要 · Abstract (English)
Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approaches for optimizing complementarity-determining regions (CDRs) to improve developability properties operate in the raw data space, leading to excessively costly evaluations due to the inefficient search process. To address this, we propose LatEnt blAck-box Design (LEAD), a sequence-structure co-design framework that optimizes both sequence and structure within their shared latent space. Optimizing shared latent codes can not only break through the limitations of existing methods, but also ensure synchronization of different modality designs. Particularly, we design a black-box guidance strategy to accommodate real-world scenarios where many property evaluators are non-differentiable. Experimental results demonstrate that our LEAD achieves superior optimization performance for both single and multi-property objectives. Notably, LEAD reduces query consumption by a half while surpassing baseline methods in property optimization. The code is available at https://github.com/EvaFlower/LatEnt-blAck-box-Design.
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