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arXiv · 2510.24974

Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution

Mia Adler·Carrie Liang·Brian Peng·Oleg Presnyakov·Justin M. Baker·Jannelle Lauffer·Himani Sharma·Barry Merriman
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Abstract

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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Mia Adler, Carrie Liang, Brian Peng, Oleg Presnyakov, Justin M. Baker, Jannelle Lauffer, Himani Sharma, Barry Merriman. 2025-10-28. Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution. https://arxiv.org/abs/2510.24974

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