Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision

• URL: http://arxiv.org/abs/2607.16263v1
• Date: Mon, 29 Jun 2026 03:34:12 GMT
• Title: Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision
• Abstract summary: We propose a unified preference-based learning framework that integrates scarce quantitative expression data with large-scale weak positive supervision from immunization data.<n>We adapt Direct Preference Optimization to protein language models by introducing a union-masked log-likelihood approximation and IMGT-based alignment.<n>Our results demonstrate that preference learning can effectively learn from weak supervision, providing a scalable solution for antibody expressibility optimization in data-constrained settings.
• Score: 1.5623752145311105
• License: http://creativecommons.org/licenses/by/4.0/
• 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: https://kisoji-biotechnology-inc.github.io/Preference-Expression-Ranking/.

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