Just on project in the domain of Medical AI our lab has carried out. This is ongoing work with Kisogi Biotech Inc. on explores ways to find promising proteins and antibodies for development of new cancer drugs.
Our Papers on Antibody Discovery
Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision
In
Forty-third International Conference on Machine Learning (ICML).
PMLR,
Seoul, South Korea.
Jul,
2026.
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.