Peilin Zhao

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.

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.

This paper introduces PMDM, a dual diffusion model for generating 3D bioactive molecules and optimizing leads for specific protein targets.

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

This paper introduces a Graph Enhanced Transformer model for retrosynthesis prediction, achieving better performance than a vanilla Transformer by incorporating molecular graph information.