Yunchao Liu

The paper validates and analyzes 12,000 AI-designed CAR-T binders from a global competition, identifying successful designs and factors improving their performance.

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 paper evaluates combining graph neural networks with expert descriptors for ligand-based virtual screening, finding that simpler GNNs with descriptors perform comparably to complex ones and that expert descriptors excel in scaffold-split scenarios.

The paper develops a machine learning model to predict MHC-I binding affinity for peptides with non-canonical amino acids and evaluates its performance against existing methods.

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.

The paper introduces a chirality-aware graph neural network for drug discovery that improves predictive performance and interpretability in quantitative structure activity relationship modeling.