Xiaohan Kuang

The paper introduces a protein language model-based method to predict conformational B-cell epitopes on antigen structures, outperforming existing methods on benchmark datasets.

SuperMetal predicts metal-binding sites in proteins with high precision and efficiency, outperforming existing models for zinc ions.

This study uses all-atom simulations and AI to identify fibril-prone peptide sequences, achieving high accuracy in predicting amyloid-like behavior.

SuperWater predicts water molecule positions on protein structures using a generative AI framework, achieving high accuracy in crystal water coverage and prediction precision.

This paper develops a deep learning model to analyze antibody-antigen interactions, achieving high accuracy in distinguishing antibody-antigen complexes from other protein complexes and identifying antigens from epitope information.