{"ID":2849337,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24974","arxiv_id":"2510.24974","title":"Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution","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.","short_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 un...","url_abs":"https://arxiv.org/abs/2510.24974","url_pdf":"https://arxiv.org/pdf/2510.24974v2","authors":"[\"Mia Adler\",\"Carrie Liang\",\"Brian Peng\",\"Oleg Presnyakov\",\"Justin M. Baker\",\"Jannelle Lauffer\",\"Himani Sharma\",\"Barry Merriman\"]","published":"2025-10-28T21:13:37Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}