Jiaxiang Wu

IgGM is a generative model that designs functional antibodies and nanobodies by predicting sequences and structures simultaneously for a given antigen.

This paper introduces tFold-Ab and tFold-Ag for fast and accurate prediction of antibody and antibody-antigen complex structures, showing improved speed and performance over AlphaFold-Multimer.

tFold-Ab predicts antibody structures end-to-end without sequence homologs, achieving accuracy for both backbone and side-chain conformations.

This paper introduces SE(3)-Fold, an end-to-end protein folding method using SE(3)-equivariant energy-based models, achieving comparable accuracy to state-of-the-art methods with significant speed improvements.