Antibody Discovery

Using Data Science, AI, and ML to improve search for promising antibody drugs

This project spans multiple modalities of medical imaging and multiple types of modelling to empower medical experts.

Our Papers on Antibody Discovery

ICML

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

Josh Qixuan Sun, Morteza Babaie, Wenyang Hou, Mark Crowley, and David Young.

In Forty-third International Conference on Machine Learning (ICML). PMLR, Seoul, South Korea. Jul, 2026.

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Antibody expression ranking is a critical task in antibody design, yet its modeling 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.