用贝叶斯优化同时优化抗体多个特性，提升设计效率。
BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
- 结合不确定性建模与遗传算法，联合优化抗体多属性。
- 在多个指标上表现优于传统方法，尤其在高维序列空间中优势明显。
- 适合药物研发中需高效筛选抗体候选物的团队使用。
抗体先导化合物优化本质上是药物发现中的多目标挑战。在多种类药性特征间取得平衡对候选药物的可行性至关重要，而随着目标特性的增多，搜索难度呈指数级增长。日益丰富的体外预测工具呼唤一种高效的联合优化方法，以克服资源密集型的串行筛选流程。本文提出BOAT，一个用于多属性抗体工程的通用贝叶斯优化框架。该‘即插即用’框架将不确定性感知的代理模型与遗传算法相结合，实现多种预测抗体特性的同时优化，并支持对序列空间的高效探索。通过系统性基准测试对比遗传算法及新兴生成学习方法，我们展示了其在多目标蛋白质优化任务中与顶尖方法相当的性能。研究识别出代理驱动优化显著优于昂贵生成方法的适用场景，并确立了序列维度和评估成本带来的实际限制。
原文摘要 · Abstract (English)
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.
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