Yidong Song

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.

This paper introduces TFDesign-sdAb, a deep learning framework for designing therapeutic single-domain antibodies with tailored functionalities, achieving high success rates in generating Protein A-binding sdAbs.

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.

GLMSite identifies DNA and RNA-binding sites using geometric graph learning on ESMFold-predicted structures, outperforming sequence-based methods and matching structure-based approaches.