Qihong Yan

This paper introduces InterAb and InterAb-Opt, computational frameworks that predict and optimize broadly neutralizing antibodies using all-atom modeling and pre-trained language models, showing significant improvements in binding affinity and neutralization activity.

This study identifies structural features of affinity-matured R1-32-like antibodies that enable tolerance to SARS-CoV-2 mutations and reveals an ultrapotent antibody with broad variant activity.

The paper uses AI to identify convergent somatic hypermutation patterns in antibodies, enabling the discovery of variant-resilient broadly neutralizing antibodies from early-pandemic data.

IgGM is a generative model for antibody design that produces high-affinity antibodies through tasks like structure prediction and affinity maturation, validated on multiple antigens.

The paper identifies public antibodies using VL6-57 light chains that target a conserved epitope on SARS-CoV-2 spike protein, showing how Omicron variants evolved to escape these antibodies.