Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, and this search becomes exponentially challenging as desired properties grow. The ever-growing zoo of sophisticated in silico tools for predicting antibody properties calls for an efficient joint optimization procedure to overcome resource-intensive sequential filtering pipelines. We present BOAT, a versatile Bayesian optimization framework for multi-property antibody engineering. Our `plug-and-play' framework couples uncertainty-aware surrogate modeling with a genetic algorithm to jointly optimize various predicted antibody traits while enabling efficient exploration of sequence space. Through systematic benchmarking against genetic algorithms and newer generative learning approaches, we demonstrate competitive performance with state-of-the-art methods for multi-objective protein optimization. We identify clear regimes where surrogate-driven optimization outperforms expensive generative approaches and establish practical limits imposed by sequence dimensionality and oracle costs.

翻译：抗体先导优化本质上是药物发现中的多目标挑战。在兼具药物相似性的不同特性间取得平衡，对于开发可行候选药物至关重要，而随着所需特性的增加，这种搜索呈指数级复杂化。日益庞大的抗体特性预测计算工具库，亟需高效的联合优化流程来取代资源密集型的顺序筛选管道。我们提出BOAT，一个用于多属性抗体工程的多功能贝叶斯优化框架。该"即插即用"框架将不确定性感知代理建模与遗传算法相结合，在实现高效序列空间探索的同时联合优化多种预测抗体特征。通过针对遗传算法和新型生成式学习方法的系统性基准测试，我们证明了该方法在多目标蛋白质优化中与最先进技术具有竞争性表现。我们明确了代理驱动优化优于昂贵生成方法的条件范围，并建立了由序列维度与预测成本所施加的实际性能极限。