Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911297526824960 |
|---|---|
| author | Adler, Mia Liang, Carrie Peng, Brian Presnyakov, Oleg Baker, Justin M. Lauffer, Jannelle Sharma, Himani Merriman, Barry |
| author_facet | Adler, Mia Liang, Carrie Peng, Brian Presnyakov, Oleg Baker, Justin M. Lauffer, Jannelle Sharma, Himani Merriman, Barry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution Adler, Mia Liang, Carrie Peng, Brian Presnyakov, Oleg Baker, Justin M. Lauffer, Jannelle Sharma, Himani Merriman, Barry Machine Learning 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. |
| title | Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.24974 |