Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision
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Computer Science > Machine Learning
Title:Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision
Abstract:Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data. To address this, we propose a unified preference-based learning framework that integrates scarce quantitative expression data with large-scale weak positive supervision from immunization data. We adapt Direct Preference Optimization (DPO) to protein language models by introducing a union-masked log-likelihood approximation and IMGT-based alignment, enabling efficient training on variable-length sequences. Evaluating on a diverse internal dataset of 1254 labeled sequences and 4 million unlabeled camelid-derived antibodies, we show that our method consistently outperforms baselines on most metrics. Our results demonstrate that preference learning can effectively learn from weak supervision, providing a scalable solution for antibody expressibility optimization in data-constrained settings. Project page: this https URL.
| Comments: | Accepted at ICML 2026 |
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2607.16263 [cs.LG] |
| (or arXiv:2607.16263v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16263
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