Traditional antibody language modeling assumes sequences are i.i.d. — ignoring the time-dependent process of affinity maturation.
To address this, CoSiNE explicitly models transitions: how likely is a mature antibody y to arise from a germline precursor x over time t? (2/8)
Stephen Lu
A key challenge is that antibody evolution mixes two signals.
Some mutations are frequent because they are likely under somatic hypermutation. Others occur because the antibodies carrying them are favored by selection.
For VEP & design, we want to separate these effects. (3/8)
Antibody LMs learn what looks antibody-like, but not how selection turns naive germline antibodies into strong binders.
@aakarshv1.bsky.social and I are excited to share CoSiNE, a model that learns this germline-to-mature process for variant effect prediction and antibody design. (1/8)
Huge thanks to my coauthors @aakarshv1.bsky.social, Kohei Sanno, Jiarui Lu, @matsen.bsky.social, Milind Jagota, and @yun-s-song.bsky.social! We will be presenting at ICML.
Preprint: arxiv.org/abs/2602.18982
Code: github.com/thematrixmas...
Blog: songlab-cal.github.io/cosine
(8/8)
