Yu Rong

ProTact improves protein-protein interaction prediction using a geometric graph neural network with physics-informed constraints, outperforming existing methods on benchmark datasets.

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.

The paper introduces scPROTEIN, a deep graph contrastive learning framework for single-cell proteomics that addresses uncertainty, denoising, batch effects, and embedding learning.

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