🤖 AI Summary
Optimizing antibody complementarity-determining regions (CDRs) for developability faces challenges of low search efficiency in the raw sequence space and high evaluation costs due to black-box, non-differentiable metrics (e.g., aggregation propensity, expression yield). To address this, we propose LEAD—a deep generative framework that learns a shared latent space jointly encoding sequence and structure, enabling their co-optimization. Crucially, LEAD introduces a gradient-free black-box guidance strategy, allowing efficient optimization with respect to arbitrary, non-differentiable developability objectives. In both single- and multi-objective CDR design tasks, LEAD reduces query count by over 50% compared to state-of-the-art baselines, while yielding higher-quality candidates. This work establishes a scalable, high-fidelity paradigm for joint sequence–structure antibody design, advancing computational antibody engineering.
📝 Abstract
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.