用排序构象分组建模，区分抗体设计中的构象与认知不确定性。
Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution
- 按构象排序分组，每组独立训练神经网络委员会。
- 在新冠抗体对接任务中，性能优于传统单一模型策略。
- 适合需要高精度构象建模的抗体药物发现场景。
机器学习辅助的定向进化（MLDE）是高效探索抗体适应度景观的强大工具。许多结构感知的MLDE流程依赖单一构象或跨所有构象共享的单一委员会，限制了对构象不确定性与认知不确定性的分离能力。本文提出一种排名条件委员会（RCC）框架，利用排序后的构象为每个排名分配一个深度神经网络委员会。该设计实现了对认知不确定性与构象不确定性的合理分离。我们在SARS-CoV-2抗体对接任务上验证了RCC-MLDE方法，结果表明其显著优于基线策略。研究为治疗性抗体发现提供了可扩展的路径，同时直接应对构象不确定性建模的挑战。
原文摘要 · Abstract (English)
Machine Learning-assisted directed evolution (MLDE) is a powerful tool for efficiently navigating antibody fitness landscapes. Many structure-aware MLDE pipelines rely on a single conformation or a single committee across all conformations, limiting their ability to separate conformational uncertainty from epistemic uncertainty. Here, we introduce a rank -conditioned committee (RCC) framework that leverages ranked conformations to assign a deep neural network committee per rank. This design enables a principled separation between epistemic uncertainty and conformational uncertainty. We validate our RCC-MLDE approach on SARS-CoV-2 antibody docking, demonstrating significant improvements over baseline strategies. Our results offer a scalable route for therapeutic antibody discovery while directly addressing the challenge of modeling conformational uncertainty.
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